diff --git a/.gitattributes b/.gitattributes index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..7c85587b6eb9b98641c3b2d6d8bf872feb9c37f8 100644 --- a/.gitattributes +++ b/.gitattributes @@ -58,3 +58,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text # Video files - compressed *.mp4 filter=lfs diff=lfs merge=lfs -text *.webm filter=lfs diff=lfs merge=lfs -text +annotation/per_expert_raw/expert3.csv filter=lfs diff=lfs merge=lfs -text +annotation/per_expert_raw/expert4.csv filter=lfs diff=lfs merge=lfs -text +annotation/per_expert_raw/expert5.h5ad filter=lfs diff=lfs merge=lfs -text +annotation/per_expert_raw/expert7.h5ad filter=lfs diff=lfs merge=lfs -text +annotation/per_expert_raw/expert2.h5ad filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a13b7a2447fd321091809ffd23129ecb6cc67ec1 --- /dev/null +++ b/README.md @@ -0,0 +1,94 @@ +--- +license: cc-by-4.0 +language: +- en +pretty_name: SpatialAgent Human Expert Reference Data +tags: +- biology +- spatial-transcriptomics +- single-cell +- gene-panel-design +- cell-type-annotation +- benchmark +size_categories: +- 100K free-text description of the panel-design approach + {1..10}.csv # one full panel per expert (ranked gene lists) + split/{id}_top{50,100,150}.csv # top-N subsets of each panel +annotation/ + workflows.csv # id (1–8) -> cell-type & niche annotation approach + combined_annotations_anonymized.h5ad # all experts (anonymized) + model/baseline predictions + human_annotations_anonymized.h5ad # human experts only (anonymized), no model columns + per_expert_raw/ # the original per-expert annotation files, anonymized + expert{1,2,5,6,7}.h5ad + expert{3,4}.csv + expert7_niche.h5ad +``` + +See `panel_design/README.md` and `annotation/README.md` for the column-level details of +each subset. + +## Panel design (DLPFC) + +10 experts each submitted a ranked panel (typically top 50 / 100 / 150 genes) with a short +rationale per gene. Formats are heterogeneous (experts used different tools), so columns +differ between files; the common fields are a gene symbol, a ranking/priority, and a +free-text reasoning column. `split/` holds the top-50/100/150 truncations used for +size-matched evaluation. Workflows range from purely algorithmic (Persist, greedy kNN +reconstruction) to literature-driven marker curation — see `panel_design/workflows.csv`. + +## Annotation (developing human heart, MERFISH) + +8 experts annotated the same 228,633 cells. The two combined `.h5ad` objects share an +identical cell index and embeddings: + +- `X` — log1p-normalized expression (238 genes); `layers['raw_count']` — raw counts. +- `obsm` — `X_pca`, `X_umap`, `spatial` (tissue coordinates). +- Per-expert columns: `cell_type_tier{1,2,3}_expert{N}`, `tissue_niche_tier{1,2}_expert{N}`, + and consolidated `cell_type_expert{N}` / `tissue_niche_expert{N}`. +- Consensus reference labels: `cell_type`, `tissue_niche`. + +`combined_annotations_anonymized.h5ad` additionally contains model / baseline predictions +(`cell_type_agent`, `tissue_niche_agent`, `cell_type_gpt`, `cell_type_sctab`, +`cell_type_popv`, `cell_type_biomni_run_{1,2,3}`, `cell_type_spatialagent_run_4`) for direct +benchmarking; `human_annotations_anonymized.h5ad` is the human-only subset (those columns +dropped). `per_expert_raw/` preserves each expert's original file (with their native, +heterogeneous column schema) for full transparency. + +### Caveats +- **annotation expert 1** did not produce tissue-niche labels (niche fields are empty/NA). +- **annotation expert 3**'s labels are of uncertain origin and are likely mis-ordered — use with care. +- **annotation expert 8** has no standalone raw file; their annotations exist only inside the combined objects. +- **panel expert 3** submitted a previously designed panel for the wrong tissue. + +## License & citation + +Released under **CC-BY-4.0** (adjust if your venue requires otherwise). If you use this +data, please cite the SpatialAgent paper. The two `workflows.csv` files correspond to the +Extended Data tables describing human-scientist workflows. diff --git a/annotation/README.md b/annotation/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9b7a04c484ab79a50efd9e1380997e6064a591bf --- /dev/null +++ b/annotation/README.md @@ -0,0 +1,57 @@ +# Cell-type & tissue-niche annotation — human expert reference + +8 human scientists annotated the **same** developing-human-heart MERFISH dataset +(228,633 cells × 238 genes). Identities are removed; experts are numbered **1–8** (this +numbering is independent of the panel-design task). Per-expert methodology is in +[`workflows.csv`](workflows.csv). + +## Files + +| File | Contents | +| --- | --- | +| `workflows.csv` | `id, cell_type_workflow, niche_workflow` — each expert's approach | +| `combined_annotations_anonymized.h5ad` | All 8 experts (anonymized) **+ model/baseline predictions** | +| `human_annotations_anonymized.h5ad` | Human experts only (model/baseline columns dropped) | +| `per_expert_raw/expert{N}.h5ad` / `.csv` | Each expert's original file, anonymized (native schema) | +| `per_expert_raw/expert7_niche.h5ad` | Expert 7's tissue-niche annotation (separate source file) | + +## Combined object structure + +Both combined `.h5ad` files share one cell index and embeddings: + +- `X` — log1p-normalized expression (238 genes) +- `layers['raw_count']` — raw counts +- `obsm` — `X_pca`, `X_umap`, `spatial` + +**Per-expert annotation columns** (N = 1..8): + +``` +cell_type_tier1_expert{N} cell_type_tier2_expert{N} [cell_type_tier3_expert{N}] +tissue_niche_tier1_expert{N} tissue_niche_tier2_expert{N} +cell_type_expert{N} tissue_niche_expert{N} # consolidated single-label +``` +Tier 3 is present only for experts who provided it (cell type: experts 2, 6, 7; niche: expert 7). +Expert 6 additionally has `cell_type_main_expert6`. + +**Reference / shared columns:** `cell_type`, `tissue_niche` (consensus labels), +plus technical fields (`sample_id`, `batch`, `n_counts`, `leiden`, and cluster features). + +**Model/baseline columns** (only in `combined_annotations_anonymized.h5ad`): +`cell_type_agent`, `tissue_niche_agent`, `cell_type_gpt`, `cell_type_sctab`, +`cell_type_popv`, `cell_type_biomni_run_{1,2,3}`, `cell_type_spatialagent_run_4`. + +## Loading + +```python +import anndata as ad +adata = ad.read_h5ad("annotation/combined_annotations_anonymized.h5ad") +adata.obs["cell_type_tier1_expert5"] # one expert's tier-1 cell types +adata.layers["raw_count"] # raw counts +``` + +## Caveats +- **Expert 1** did not perform tissue-niche annotation (niche fields are empty/NA). +- **Expert 3**'s labels are of uncertain origin and likely mis-ordered — use with care. +- **Expert 8** has no standalone raw file; their annotations live only in the combined objects. +- `per_expert_raw/` files keep each expert's **native, heterogeneous** column names + (only the filename was anonymized; no scientist name appears in any column or value). diff --git a/annotation/per_expert_raw/expert2.h5ad b/annotation/per_expert_raw/expert2.h5ad new file mode 100644 index 0000000000000000000000000000000000000000..911e200097a28b1637b07f429ab23d59e5adfe5e --- /dev/null +++ b/annotation/per_expert_raw/expert2.h5ad @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4ff30ec18867e4ce94943ed60ff227166c84c15f83e912edf9c25bb3c4e2d11b +size 362532421 diff --git a/annotation/per_expert_raw/expert3.csv b/annotation/per_expert_raw/expert3.csv new file mode 100644 index 0000000000000000000000000000000000000000..f7226de806c2a73e5d66c16100a95ebdf18a2014 --- /dev/null +++ b/annotation/per_expert_raw/expert3.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ae0965dd6eafe199c9c3aabbfb67e9f30d39a5c95b19cf81740d295086fbf184 +size 40122529 diff --git a/annotation/per_expert_raw/expert4.csv b/annotation/per_expert_raw/expert4.csv new file mode 100644 index 0000000000000000000000000000000000000000..7077b8130be4e386beef0e0ec3ec04a1a2aa1455 --- /dev/null +++ b/annotation/per_expert_raw/expert4.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2eea9ae4902ee03eb9db290fff905a528984a23381906cc1767d1319878a365f +size 39013151 diff --git a/annotation/per_expert_raw/expert5.h5ad b/annotation/per_expert_raw/expert5.h5ad new file mode 100644 index 0000000000000000000000000000000000000000..606b3e8aa29064fc9d27e89afed4ae4a78ebca6e --- /dev/null +++ b/annotation/per_expert_raw/expert5.h5ad @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:42b6bf59fce45c411485361abb8a465202c49c0541a93403ec5d47103db8ed9c +size 332303237 diff --git a/annotation/per_expert_raw/expert7.h5ad b/annotation/per_expert_raw/expert7.h5ad new file mode 100644 index 0000000000000000000000000000000000000000..28e57cda733b5956b89f26e5bd28de238e0f2141 --- /dev/null +++ b/annotation/per_expert_raw/expert7.h5ad @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:55875bd147ae95013cfea2cc017e0c7ed8f702b67ad4ee9438d25365c4e00390 +size 357686236 diff --git a/annotation/workflows.csv b/annotation/workflows.csv new file mode 100644 index 0000000000000000000000000000000000000000..4bd806dd5bfc17a6eef332463032b87de81e2b11 --- /dev/null +++ b/annotation/workflows.csv @@ -0,0 +1,9 @@ +id,cell_type_workflow,niche_workflow +1,Annotated based on gene co-expression patterns.,NA +2,Leiden clustering with majority voting for consensus-based cell-type annotation; provided 3 tiers of annotation.,"Labeled niches from cell annotations with clear distributions (e.g. Atrium, Ventricular) for tier 1; considered spatial left/right position for tier 2." +3,Unknown.,"Unknown, likely mis-ordered annotations." +4,Leiden clustering and analyzed predefined marker genes in clusters; typically a single marker gene to differentiate cell types; projected cell types spatially and used position for final annotation.,Used UTAG for spatial clustering; labeled structures based on position and provided anatomical image. +5,Leiden clustering and analyzed expression of predefined marker genes in clusters; multiple genes per cell type; mapped both major cell type and subtypes.,"Used UTAG for spatial clustering; labeled structures based on position, provided anatomical image and additional sources." +6,"Label transfer using TACCO with an scRNA-seq reference of human heart; projected cell types spatially; Leiden clustering and DEG for marker genes, using key markers for second-tier annotation.",Used UTAG for spatial clustering; labeled structures based on position and provided anatomical image. +7,Leiden clustering with manual annotation using marker gene sets and DEG (per Scanpy tutorial); projected cell types spatially; used spatial position and key marker expression for final annotation; provided 3-tier annotation.,Used UTAG for spatial clustering; labeled structures based on position and anatomical knowledge of heart (e.g. 'chamber wall is thicker on the left ventricle'). +8,Combined annotation on Leiden clusters with CellTypist-transferred labels as reference.,Used UTAG for spatial clustering. diff --git a/panel_design/1.csv b/panel_design/1.csv new file mode 100644 index 0000000000000000000000000000000000000000..0dcf98b6b2790ca4bab6b05b4306bfd3dc5e7cfb --- /dev/null +++ b/panel_design/1.csv @@ -0,0 +1,151 @@ +Gene symbol,Ranking,Annotation & reasoning,Additional note +NeuN,1-50,Pan Neuron marker often used for ISH, +SST,1-50,Defines SST+ Interneurons, +PVALB,1-50,Identifies inhibitory interneurons, +CLND5,1-50,Endothelial cells / Mural cells, +HBA1,1-50,Endothelial cells / Mural cells, +ASCA2,1-50,Astrocyte marker often used for Bead collection, +GFAP,1-50,Astrocyte marker , +CX3CR1 ,1-50,Microglia marker, +TMEM119,1-50,Microglial marker, +AIF1,1-50,IBA1 is often used for in situ hybridzation to label microglial cells. , +OLIG2,1-50,"Expressed by OPCs, getting cells ready for differentiation into myelin-forming oligodendocytes. ", +CD22,1-50,Expressed by oligodendrocytes in huamns and binds to sialic acid-dependent ligands on microglia. , +Th,1-50,Often used by ISH of dopaminergic neurons. , +Reln,1-50,"Genes that seperate layers of cortical region (Huuki-Myers, BioRxiv, 2023)", +Aqp4,1-50,"Genes that seperate layers of cortical region (Huuki-Myers, BioRxiv, 2023)", +SPARC,1-50,"Genes that seperate layers of cortical region (Huuki-Myers, BioRxiv, 2023)", +HTRA1,1-50,"Genes that seperate layers of cortical region (Huuki-Myers, BioRxiv, 2023)", +VIP,1-50,Labels interneurons in PFC that signal and inhibits SST+ interneurons, +Fos,1-50,Activation-related genes from neurons. , +Arc,1-50,Activation-related genes from neurons. , +Egr1,1-50,Activation-related genes from neurons. , +BDNF,1-50,"For survival mechanisms of neuronal homeostasis, often associated with disease. ", +ADORA1,1-50,Neurotransmitter receptors, +HTR1A,1-50,Neurotransmitter receptors, +HTR2A,1-50,Neurotransmitter receptors, +HTR3A,1-50,Neurotransmitter receptors, +HTR4,1-50,Neurotransmitter receptors, +DRD1,1-50,Neurotransmitter receptors, +DRD2,1-50,Neurotransmitter receptors, +DRD4,1-50,Neurotransmitter receptors, +NR3C1,1-50,Neurotransmitter receptors, +NPY1R,1-50,Neurotransmitter receptors, +OXTR,1-50,Expressed by SST+ neurons to respond to , +GRIN2B,1-50,Receptors common for neural plasticity, +GABRA1,1-50,Receptors common for neural plasticity, +GRIA1,1-50,Receptors common for neural plasticity, +NEDD4,1-50,Marker for excitatory neurons, +FBXO2,1-50,Marker for excitatory neurons, +mTOR,1-50,Marker for excitatory neurons, +DDIT4,1-50,Marker for excitatory neurons, +TH,1-50,Marker for excitatory neurons, +PDGFRA,1-50,OPCs, +GAD1,1-50,"Glutamate Decarboxylase 1, catalyzing production from L-glut. ", +CHAT,1-50,Neuron enzyme for ACh, +GRIN2A,1-50,NMDA receptors, +GABRD,1-50,GABA receptors, +GABRA1,1-50,GABA receptors, +TREM2,1-50,microglial marker, +CSF1R,1-50,microglial marker, +IL10,1-50,Microglia function, +EFNA5,50-100,Important pathways for neural plasticity and synaptic homeostasis. , +EPHA5,50-100,Important pathways for neural plasticity and synaptic homeostasis. , +FYN,50-100,Important pathways for neural plasticity and synaptic homeostasis. , +CARMN,50-100,Endothelial cells: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +ITIH5,50-100,Endothelial cells: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +MECOM,50-100,Endothelial cells: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +EBF1,50-100,Endothelial cells: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +VWF,50-100,Endothelial cells: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +LINC02712,50-100,Microglia: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +ITGAX,50-100,Microglia: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +BLNK,50-100,Microglia: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +CSF2RA,50-100,Microglia: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +FOLH1,50-100,Oligodendrocytes: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +LINC01608,50-100,Oligodendrocytes: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +SLC5A11,50-100,Oligodendrocytes: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +OPC,50-100,OPC: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +AC004852.2,50-100,OPC: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +FERMT1,50-100,OPC: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +COL9A1,50-100,OPC: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +STK32A,50-100,OPC: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +FGF13,50-100,Excitatory Layer 2 or 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +SLC12A8,50-100,Excitatory Layer 2 or 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +DCBLD2,50-100,Excitatory Layer 2 or 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +MPC1,50-100,Excitatory Layer 2 or 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +LINC02296,50-100,Excitatory Layer 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +AC008674.1,50-100,Excitatory Layer 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +CLRA3,50-100,Excitatory Layer 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +CPHR1,50-100,Excitatory Layer 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +FBXL16,50-100,Excitatory Layer 3/4/5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +MAP1A,50-100,Excitatory Layer 3/4/5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +UBB,50-100,Excitatory Layer 3/4/5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +ENC1,50-100,Excitatory Layer 3/4/5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +TSHZ2,50-100,Excitatory Layer 4 Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +VGF,50-100,Excitatory Layer 4 Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +UBE2E3,50-100,Excitatory Layer 4 Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +APP003066.1,50-100,Excitatory Layer 5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +COL12A1,50-100,Excitatory Layer 5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +TRABD2A,50-100,Excitatory Layer 5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +TLL1,50-100,Excitatory Layer 5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. , +LINC00343,50-100,Excitatory L5/6, +THEMIS,50-100,Excitatory L5/6, +AC015943.1,50-100,Excitatory L5/6, +LINC02718,50-100,Excitatory L6: Top genes from Huuki-Myers, +MCTP2,50-100,Excitatory L6: Top genes from Huuki-Myers, +AC006299.1,50-100,Excitatory L6: Top genes from Huuki-Myers, +DPP4,50-100,Excitatory L6: Top genes from Huuki-Myers, +MYO3B,50-100,Inhibitory neurons: Top genes from Huuki-Myers, +SLC27A6,50-100,Inhibitory neurons: Top genes from Huuki-Myers, +MINAR1,50-100,Inhibitory neurons: Top genes from Huuki-Myers, +BTBD11,50-100,Inhibitory neurons: Top genes from Huuki-Myers, +FBN2,100-150,Inhibitory neurons: Top genes from Huuki-Myers, +GRIP2,100-150,Inhibitory neurons: Top genes from Huuki-Myers, +COMT,100-150,Enzymes that degrade neurotransmitters, +SLC6A3,100-150,Dopamine transporter, +MAOA,100-150,Breakdown of neurotransmitters, +CREB1,100-150,Neural activation related genes, +FOS,100-150,Neural activation related genes, +JUNB,100-150,Neural activation related genes, +NFAT1,100-150,Neural activation related genes, +CRTC1,100-150,Neural activation related genes, +CAMK2A,100-150,Neural activation related genes, +CAMK1D,100-150,Neural activation related genes, +APOE4,100-150,"Alzhiemers, microglia. ", +SHANK3,100-150,Genes altered in ASD, +RAC1,100-150,Genes altered in ASD, +PAK,100-150,Genes altered in ASD, +COFILIN,100-150,Genes altered in ASD, +NR2A,100-150,Genes altered in Schizophernia, +GAD67,100-150,Genes altered in Schizophernia, +CALM2,100-150,Genes altered in MDD brains also associated with synatic function and reduced spine density in layers II/III of DLPFC, +SYN1,100-150,Genes altered in MDD brains also associated with synatic function and reduced spine density in layers II/III of DLPFC, +RAB3A,100-150,Genes altered in MDD brains also associated with synatic function and reduced spine density in layers II/III of DLPFC, +RAB4B,100-150,Genes altered in MDD brains also associated with synatic function and reduced spine density in layers II/III of DLPFC, +TUBB4,100-150,Genes altered in MDD brains also associated with synatic function and reduced spine density in layers II/III of DLPFC, +NR2B,100-150,Genes altered in MDD brains also associated with synatic function and reduced spine density in layers II/III of DLPFC, +PSD96,100-150,Genes altered in MDD brains also associated with synatic function and reduced spine density in layers II/III of DLPFC, +cpg15,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ", +NTRK2,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ", +HLA-A,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ", +PLK2,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ", +Homer1,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ", +Arc,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ", +MIR134,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ", +Mecp2,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ", +MEF22c,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ", +CARF,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ", +HLA-B,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ", +HLA-C,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ", +KIT,100-150,Inhibitory neurons: Top genes from Huuki-Myers, +PLXDC2,100-150,Top DEG from Jupyter of microglia, +DOCK4,100-150,Top DEG from Jupyter of microglia, +DOCK8,100-150,Top DEG from Jupyter of microglia, +AdGRV1,100-150,Top DEG from jupyter of astrocytes, +SLC1A2,100-150,Top DEG from jupyter of astrocytes, +MSI2,100-150,Top DEG from jupyter of astrocytes, +GPC5,100-150,Top DEG from jupyter of astrocytes, +SORCS3,100-150,Caudal Ganglionic derived GABAergic cortical interneurons from Top DEG from Jupyter, +ADARB2,100-150,Caudal Ganglionic derived GABAergic cortical interneurons from Top DEG from Jupyter, +CXCL14,100-150,Caudal Ganglionic derived GABAergic cortical interneurons from Top DEG from Jupyter, +SLC35F4,100-150,Caudal Ganglionic derived GABAergic cortical interneurons from Top DEG from Jupyter, diff --git a/panel_design/10.csv b/panel_design/10.csv new file mode 100644 index 0000000000000000000000000000000000000000..519d6b156eed48fa186ae70a56718939654dd8dc --- /dev/null +++ b/panel_design/10.csv @@ -0,0 +1,151 @@ +Unnamed: 0,Gene Symbol,Ranking,Annotation & Reasoning,Additional Comment +0,KCNIP4,top 50,Top DE genes for cell type - L2/3-6 intratelencephalic projecting glutamatergic neuron in the provided dataset, +1,R3HDM1,top 50,Top DE genes for cell type - L2/3-6 intratelencephalic projecting glutamatergic neuron in the provided dataset, +2,SATB2,top 50,Top DE genes for cell type - L2/3-6 intratelencephalic projecting glutamatergic neuron in the provided dataset, +3,VAT1L,top 50,Top DE genes for cell type - L5 extratelencephalic projecting glutamatergic cortical neuron in the provided dataset, +4,CLEC2L,top 50,Top DE genes for cell type - L5 extratelencephalic projecting glutamatergic cortical neuron in the provided dataset, +5,LMO7,top 50,Top DE genes for cell type - L5 extratelencephalic projecting glutamatergic cortical neuron in the provided dataset, +6,HS3ST4,top 50,Top DE genes for cell type - L6b glutamatergic cortical neuron in the provided dataset | Top DE genes for cell type - corticothalamic-projecting glutamatergic cortical neuron in the provided dataset | Top 20-50 HVG Genes, +7,ZFHX3,top 50,Top DE genes for cell type - L6b glutamatergic cortical neuron in the provided dataset, +8,TLE4,top 50,Top DE genes for cell type - L6b glutamatergic cortical neuron in the provided dataset, +9,ADGRV1,top 50,Top DE genes for cell type - astrocyte of the cerebral cortex in the provided dataset | Top 20 HVG Genes, +10,SLC1A3,top 50,Top DE genes for cell type - astrocyte of the cerebral cortex in the provided dataset | Top 20 HVG Genes, +11,SLC1A2,top 50,Top DE genes for cell type - astrocyte of the cerebral cortex in the provided dataset | Top 20 HVG Genes, +12,SORCS3,top 50,Top DE genes for cell type - caudal ganglionic eminence derived GABAergic cortical interneuron in the provided dataset, +13,ADARB2,top 50,Top DE genes for cell type - caudal ganglionic eminence derived GABAergic cortical interneuron in the provided dataset | Top DE genes for cell type - vip GABAergic cortical interneuron in the provided dataset | Top 20 HVG Genes, +14,CXCL14,top 50,"Top DE genes for cell type - caudal ganglionic eminence derived GABAergic cortical interneuron in the provided dataset | Top DE genes for cell type - sncg GABAergic cortical interneuron in the provided dataset | Top 20 HVG Genes | Top 2 Marker Genes for cell type - Neuroendocrine cells in human brain, according to PanglaoDB database", +15,ATP10A,top 50,Top DE genes for cell type - cerebral cortex endothelial cell in the provided dataset, +16,ABCB1,top 50,Top DE genes for cell type - cerebral cortex endothelial cell in the provided dataset, +17,MECOM,top 50,Top DE genes for cell type - cerebral cortex endothelial cell in the provided dataset, +18,CNTN5,top 50,Top DE genes for cell type - chandelier pvalb GABAergic cortical interneuron in the provided dataset, +19,ZNF385D,top 50,Top DE genes for cell type - chandelier pvalb GABAergic cortical interneuron in the provided dataset | Top 20-50 HVG Genes, +20,RORA,top 50,Top DE genes for cell type - chandelier pvalb GABAergic cortical interneuron in the provided dataset, +21,TRPM3,top 50,Top DE genes for cell type - corticothalamic-projecting glutamatergic cortical neuron in the provided dataset, +22,SEMA3E,top 50,Top DE genes for cell type - corticothalamic-projecting glutamatergic cortical neuron in the provided dataset, +23,FGF13,top 50,Top DE genes for cell type - lamp5 GABAergic cortical interneuron in the provided dataset, +24,FGF14,top 50,Top DE genes for cell type - lamp5 GABAergic cortical interneuron in the provided dataset, +25,MYO16,top 50,Top DE genes for cell type - lamp5 GABAergic cortical interneuron in the provided dataset, +26,PLXDC2,top 50,Top DE genes for cell type - microglial cell in the provided dataset | Top 20-50 HVG Genes, +27,DOCK4,top 50,Top DE genes for cell type - microglial cell in the provided dataset, +28,DOCK8,top 50,Top DE genes for cell type - microglial cell in the provided dataset | Top 20 HVG Genes, +29,NPSR1-AS1,top 50,Top DE genes for cell type - near-projecting glutamatergic cortical neuron in the provided dataset, +30,ASIC2,top 50,Top DE genes for cell type - near-projecting glutamatergic cortical neuron in the provided dataset, +31,ITGA8,top 50,Top DE genes for cell type - near-projecting glutamatergic cortical neuron in the provided dataset, +32,MBP,top 50,Top DE genes for cell type - oligodendrocyte in the provided dataset | Top 20 HVG Genes, +33,ST18,top 50,Top DE genes for cell type - oligodendrocyte in the provided dataset | Top 20 HVG Genes, +34,CTNNA3,top 50,Top DE genes for cell type - oligodendrocyte in the provided dataset | Top 20-50 HVG Genes, +35,LHFPL3,top 50,Top DE genes for cell type - oligodendrocyte precursor cell in the provided dataset | Top 20 HVG Genes, +36,DSCAM,top 50,Top DE genes for cell type - oligodendrocyte precursor cell in the provided dataset, +37,PTPRZ1,top 50,Top DE genes for cell type - oligodendrocyte precursor cell in the provided dataset, +38,PPARGC1A,top 50,Top DE genes for cell type - pvalb GABAergic cortical interneuron in the provided dataset, +39,FGF12,top 50,Top DE genes for cell type - pvalb GABAergic cortical interneuron in the provided dataset, +40,KCNC2,top 50,Top DE genes for cell type - pvalb GABAergic cortical interneuron in the provided dataset, +41,INPP4B,top 50,Top DE genes for cell type - sncg GABAergic cortical interneuron in the provided dataset, +42,FSTL5,top 50,Top DE genes for cell type - sncg GABAergic cortical interneuron in the provided dataset, +43,GRIK1,top 50,Top DE genes for cell type - sst GABAergic cortical interneuron in the provided dataset | Top 20 HVG Genes, +44,XKR4,top 50,Top DE genes for cell type - sst GABAergic cortical interneuron in the provided dataset, +45,KIAA1217,top 50,Top DE genes for cell type - sst GABAergic cortical interneuron in the provided dataset, +46,DLC1,top 50,Top DE genes for cell type - vascular leptomeningeal cell in the provided dataset, +47,ATP1A2,top 50,Top DE genes for cell type - vascular leptomeningeal cell in the provided dataset | Top 20-50 HVG Genes, +48,EBF1,top 50,Top DE genes for cell type - vascular leptomeningeal cell in the provided dataset | Top 20-50 HVG Genes, +49,RGS12,top 50,Top DE genes for cell type - vip GABAergic cortical interneuron in the provided dataset, +50,SYNPR,top 50-100,Top DE genes for cell type - vip GABAergic cortical interneuron in the provided dataset, +51,NPY,top 50-100,Top 20 HVG Genes, +52,ERBB4,top 50-100,Top 20 HVG Genes, +53,PLP1,top 50-100,Top 20 HVG Genes, +54,RELN,top 50-100,Top 20 HVG Genes, +55,CCL3,top 50-100,Top 20 HVG Genes, +56,GPC5,top 50-100,Top 20 HVG Genes, +57,SGCZ,top 50-100,Top 20 HVG Genes, +58,ARHGAP24,top 50-100,Top 20 HVG Genes, +59,RNF220,top 50-100,Top 20 HVG Genes, +60,APBB1IP,top 50-100,Top 20 HVG Genes, +61,SYT1,top 50-100,"Top 1 Marker Genes for cell type - Adrenergic neurons in human brain, according to PanglaoDB database", +62,NUCB2,top 50-100,"Top 1 Marker Genes for cell type - Anterior pituitary gland cells in human brain, according to PanglaoDB database", +63,VIM,top 50-100,"Top 1 Marker Genes for cell type - Astrocytes in human brain, according to PanglaoDB database | Top 1 Marker Genes for cell type - Bergmann glia in human brain, according to PanglaoDB database", +64,PABPN1,top 50-100,"Top 1 Marker Genes for cell type - Cajal-Retzius cells in human brain, according to PanglaoDB database", +65,ACLY,top 50-100,"Top 1 Marker Genes for cell type - Cholinergic neurons in human brain, according to PanglaoDB database", +66,TTR,top 50-100,"Top 1 Marker Genes for cell type - Choroid plexus cells in human brain, according to PanglaoDB database", +67,NR4A2,top 50-100,"Top 1 Marker Genes for cell type - Dopaminergic neurons in human brain, according to PanglaoDB database", +68,TM4SF1,top 50-100,"Top 1 Marker Genes for cell type - Ependymal cells in human brain, according to PanglaoDB database", +69,GADD45B,top 50-100,"Top 1 Marker Genes for cell type - GABAergic neurons in human brain, according to PanglaoDB database", +70,MEIS2,top 50-100,"Top 1 Marker Genes for cell type - Glutaminergic neurons in human brain, according to PanglaoDB database", +71,SLC32A1,top 50-100,"Top 1 Marker Genes for cell type - Glycinergic neurons in human brain, according to PanglaoDB database", +72,NES,top 50-100,"Top 1 Marker Genes for cell type - Immature neurons in human brain, according to PanglaoDB database", +73,RGS10,top 50-100,"Top 1 Marker Genes for cell type - Interneurons in human brain, according to PanglaoDB database", +74,IGFBP2,top 50-100,"Top 1 Marker Genes for cell type - Meningeal cells in human brain, according to PanglaoDB database", +75,FOS,top 50-100,"Top 1 Marker Genes for cell type - Microglia in human brain, according to PanglaoDB database", +76,ISL1,top 50-100,"Top 1 Marker Genes for cell type - Motor neurons in human brain, according to PanglaoDB database", +77,S100A6,top 50-100,"Top 1 Marker Genes for cell type - Neural stem/precursor cells in human brain, according to PanglaoDB database", +78,PBX1,top 50-100,"Top 1 Marker Genes for cell type - Neuroblasts in human brain, according to PanglaoDB database", +79,SST,top 50-100,"Top 1 Marker Genes for cell type - Neuroendocrine cells in human brain, according to PanglaoDB database | Top 2 Marker Genes for cell type - GABAergic neurons in human brain, according to PanglaoDB database", +80,PNISR,top 50-100,"Top 1 Marker Genes for cell type - Neurons in human brain, according to PanglaoDB database", +81,SLC9B2,top 50-100,"Top 1 Marker Genes for cell type - Noradrenergic neurons in human brain, according to PanglaoDB database", +82,VCAN,top 50-100,"Top 1 Marker Genes for cell type - Oligodendrocyte progenitor cells in human brain, according to PanglaoDB database | Top 20-50 HVG Genes", +83,GAMT,top 50-100,"Top 1 Marker Genes for cell type - Oligodendrocytes in human brain, according to PanglaoDB database", +84,CREM,top 50-100,"Top 1 Marker Genes for cell type - Pinealocytes in human brain, according to PanglaoDB database", +85,CD3G,top 50-100,"Top 1 Marker Genes for cell type - Purkinje neurons in human brain, according to PanglaoDB database", +86,YWHAZ,top 50-100,"Top 1 Marker Genes for cell type - Pyramidal cells in human brain, according to PanglaoDB database", +87,SPRY1,top 50-100,"Top 1 Marker Genes for cell type - Radial glia cells in human brain, according to PanglaoDB database", +88,NARF,top 50-100,"Top 1 Marker Genes for cell type - Retinal ganglion cells in human brain, according to PanglaoDB database", +89,GLUL,top 50-100,"Top 1 Marker Genes for cell type - Satellite glial cells in human brain, according to PanglaoDB database", +90,STMN1,top 50-100,"Top 1 Marker Genes for cell type - Schwann cells in human brain, according to PanglaoDB database", +91,ESM1,top 50-100,"Top 1 Marker Genes for cell type - Serotonergic neurons in human brain, according to PanglaoDB database", +92,PRDX6,top 50-100,"Top 1 Marker Genes for cell type - Tanycytes in human brain, according to PanglaoDB database", +93,CPNE3,top 50-100,"Top 1 Marker Genes for cell type - Trigeminal neurons in human brain, according to PanglaoDB database", +94,DDC,top 50-100,"Top 2 Marker Genes for cell type - Adrenergic neurons in human brain, according to PanglaoDB database | Top 2 Marker Genes for cell type - Noradrenergic neurons in human brain, according to PanglaoDB database | Top 2 Marker Genes for cell type - Serotonergic neurons in human brain, according to PanglaoDB database", +95,NKTR,top 50-100,"Top 2 Marker Genes for cell type - Anterior pituitary gland cells in human brain, according to PanglaoDB database", +96,APOE,top 50-100,"Top 2 Marker Genes for cell type - Astrocytes in human brain, according to PanglaoDB database", +97,ITGB1,top 50-100,"Top 2 Marker Genes for cell type - Bergmann glia in human brain, according to PanglaoDB database", +98,SLC25A36,top 50-100,"Top 2 Marker Genes for cell type - Cajal-Retzius cells in human brain, according to PanglaoDB database", +99,BRCA1,top 50-100,"Top 2 Marker Genes for cell type - Cholinergic neurons in human brain, according to PanglaoDB database", +100,CHMP1A,top 100-150,"Top 2 Marker Genes for cell type - Choroid plexus cells in human brain, according to PanglaoDB database", +101,SMAD3,top 100-150,"Top 2 Marker Genes for cell type - Dopaminergic neurons in human brain, according to PanglaoDB database", +102,RARRES2,top 100-150,"Top 2 Marker Genes for cell type - Ependymal cells in human brain, according to PanglaoDB database", +103,GLS,top 100-150,"Top 2 Marker Genes for cell type - Glutaminergic neurons in human brain, according to PanglaoDB database", +104,SLC6A9,top 100-150,"Top 2 Marker Genes for cell type - Glycinergic neurons in human brain, according to PanglaoDB database", +105,CREB1,top 100-150,"Top 2 Marker Genes for cell type - Immature neurons in human brain, according to PanglaoDB database", +106,DHRS3,top 100-150,"Top 2 Marker Genes for cell type - Interneurons in human brain, according to PanglaoDB database", +107,DCN,top 100-150,"Top 2 Marker Genes for cell type - Meningeal cells in human brain, according to PanglaoDB database", +108,EGR1,top 100-150,"Top 2 Marker Genes for cell type - Microglia in human brain, according to PanglaoDB database", +109,NKX6-1,top 100-150,"Top 2 Marker Genes for cell type - Motor neurons in human brain, according to PanglaoDB database", +110,RBM3,top 100-150,"Top 2 Marker Genes for cell type - Neural stem/precursor cells in human brain, according to PanglaoDB database", +111,EZH2,top 100-150,"Top 2 Marker Genes for cell type - Neuroblasts in human brain, according to PanglaoDB database", +112,MEG3,top 100-150,"Top 2 Marker Genes for cell type - Neurons in human brain, according to PanglaoDB database", +113,CNP,top 100-150,"Top 2 Marker Genes for cell type - Oligodendrocyte progenitor cells in human brain, according to PanglaoDB database", +114,PTGDS,top 100-150,"Top 2 Marker Genes for cell type - Oligodendrocytes in human brain, according to PanglaoDB database | Top 20-50 HVG Genes", +115,PMEPA1,top 100-150,"Top 2 Marker Genes for cell type - Pinealocytes in human brain, according to PanglaoDB database", +116,MRPS35,top 100-150,"Top 2 Marker Genes for cell type - Purkinje neurons in human brain, according to PanglaoDB database", +117,RTN4,top 100-150,"Top 2 Marker Genes for cell type - Pyramidal cells in human brain, according to PanglaoDB database", +118,PAX6,top 100-150,"Top 2 Marker Genes for cell type - Radial glia cells in human brain, according to PanglaoDB database", +119,RBPMS,top 100-150,"Top 2 Marker Genes for cell type - Retinal ganglion cells in human brain, according to PanglaoDB database", +120,CXCL8,top 100-150,"Top 2 Marker Genes for cell type - Satellite glial cells in human brain, according to PanglaoDB database", +121,SEPT9,top 100-150,"Top 2 Marker Genes for cell type - Schwann cells in human brain, according to PanglaoDB database", +122,RGCC,top 100-150,"Top 2 Marker Genes for cell type - Tanycytes in human brain, according to PanglaoDB database", +123,DHCR24,top 100-150,"Top 2 Marker Genes for cell type - Trigeminal neurons in human brain, according to PanglaoDB database", +124,HERC2P3_ENSG00000180229,top 100-150,Top 20-50 HVG Genes, +125,CLDN5,top 100-150,Top 20-50 HVG Genes, +126,GFAP,top 100-150,Top 20-50 HVG Genes, +127,OBI1-AS1,top 100-150,Top 20-50 HVG Genes, +128,QKI,top 100-150,Top 20-50 HVG Genes, +129,CCL4,top 100-150,Top 20-50 HVG Genes, +130,MOBP,top 100-150,Top 20-50 HVG Genes, +131,MT-CO3,top 100-150,Top 20-50 HVG Genes, +132,SPP1,top 100-150,Top 20-50 HVG Genes, +133,NXPH1,top 100-150,Top 20-50 HVG Genes, +134,FAM177B,top 100-150,Top 20-50 HVG Genes, +135,HPSE2,top 100-150,Top 20-50 HVG Genes, +136,ZBTB20,top 100-150,Top 20-50 HVG Genes, +137,ID3,top 100-150,Top 20-50 HVG Genes, +138,HSPA1A,top 100-150,Top 20-50 HVG Genes, +139,CCK,top 100-150,Top 20-50 HVG Genes, +140,PDE4B,top 100-150,Top 20-50 HVG Genes, +141,SOX2-OT,top 100-150,Top 20-50 HVG Genes, +142,HTR2C,top 100-150,Top 20-50 HVG Genes, +143,CERCAM,top 100-150,Top 20-50 HVG Genes, +144,PIP4K2A,top 100-150,Top 20-50 HVG Genes, +145,COLEC12,top 100-150,Top 20-50 HVG Genes, +146,CX3CR1,top 100-150,Top 20-50 HVG Genes, +147,PCDH15,top 100-150,Top 20-50 HVG Genes, +148,PRELID2,top 100-150,Top 20-50 HVG Genes, +149,FBXL7,top 100-150,Top 20-50 HVG Genes, diff --git a/panel_design/2.csv b/panel_design/2.csv new file mode 100644 index 0000000000000000000000000000000000000000..1665dce389b1758ebd99abe8f80058b88eba8ac7 --- /dev/null +++ b/panel_design/2.csv @@ -0,0 +1,151 @@ +Gene symbol,Ranking,Annotation & Reasoning,Additional note +KCNG1,top 50,"Use Persist to select the top 50, 100, and 150 genes", +WLS,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +PRKCG,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +KCNG2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +IL1RAPL2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +PDGFC,top 50,"Use Persist to select the top 50, 100, and 150 genes", +VWC2L,top 50,"Use Persist to select the top 50, 100, and 150 genes", +SV2C,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +GRM1,top 50,"Use Persist to select the top 50, 100, and 150 genes", +ITGA8,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +PTPRZ1,top 50,"Use Persist to select the top 50, 100, and 150 genes", +NEAT1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +FSTL4,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +RTN4RL1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +ALCAM,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +NKAIN3,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +SLC6A11,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +SHISA9,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +IGSF21,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +UBASH3B,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +BRINP1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +WIF1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +CALN1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +ERICH2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +SYNPR,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +L3MBTL4,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +CARMIL1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +UBE2QL1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +SLC26A4,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +COL4A2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +HTR1F,top 50,"Use Persist to select the top 50, 100, and 150 genes", +SPOCK1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +DOCK11,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +GULP1,top 50,"Use Persist to select the top 50, 100, and 150 genes", +SLC9A9,top 50,"Use Persist to select the top 50, 100, and 150 genes", +IRS2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +FRMD3,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +ST8SIA2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +MGAT5B,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +IRAK3,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +PTPRK,top 50,"Use Persist to select the top 50, 100, and 150 genes", +SPATS2L,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +GRM8,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +SILC1,top 50,"Use Persist to select the top 50, 100, and 150 genes", +MEIS2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +TMEM144,top 50,"Use Persist to select the top 50, 100, and 150 genes", +EYA4,top 50,"Use Persist to select the top 50, 100, and 150 genes", +SLC2A1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +RGMA,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +KCNH5,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +CNTNAP3P2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +KCNIP3,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +NPNT,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +CLMP,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +PPFIBP1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +ANO2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +ASIC4,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +NXPH2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +RNF220,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +MAPK4,top 50,"Use Persist to select the top 50, 100, and 150 genes", +TRPC6,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +GRIA4,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +ZBBX,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +SHISA8,top 50,"Use Persist to select the top 50, 100, and 150 genes", +CRHBP,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +SEMA3C,top 50,"Use Persist to select the top 50, 100, and 150 genes", +PCSK6,top 50,"Use Persist to select the top 50, 100, and 150 genes", +CACNA2D1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +GNG4,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +ID2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +DPP10-AS3,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +FRAS1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +RPH3A,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +EPHA3,top 50,"Use Persist to select the top 50, 100, and 150 genes", +SEMA5A,top 50,"Use Persist to select the top 50, 100, and 150 genes", +FBXL7,top 50,"Use Persist to select the top 50, 100, and 150 genes", +PAPSS2,top 50,"Use Persist to select the top 50, 100, and 150 genes", +UNC5B,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +ANGPT1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +PRKD1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +FRMD4B,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +CTXND1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +KCNIP1,top 50,"Use Persist to select the top 50, 100, and 150 genes", +RNF152,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +SLC24A4,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +CBLN4,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +HTR2C,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +CDH20,top 50,"Use Persist to select the top 50, 100, and 150 genes", +DYSF,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +RASSF5,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +ATP1B2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +LHFPL3,top 50,"Use Persist to select the top 50, 100, and 150 genes", +NTNG1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +PELI2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +EEF1DP3,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +GREM2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +GUCY1A1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +SPHKAP,top 50,"Use Persist to select the top 50, 100, and 150 genes", +NWD2,top 50,"Use Persist to select the top 50, 100, and 150 genes", +C12orf42,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +DENND3,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +ARAP2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +LYPD6B,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +FNBP1L,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +PDE7B,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +MARCHF3,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +SIPA1L2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +RBM20,top 50,"Use Persist to select the top 50, 100, and 150 genes", +ZNF385D-AS2,top 50,"Use Persist to select the top 50, 100, and 150 genes", +KIRREL3,top 50,"Use Persist to select the top 50, 100, and 150 genes", +UTRN,top 50,"Use Persist to select the top 50, 100, and 150 genes", +TOX,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +VCAN,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +UST,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +ZNF462,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +KMO,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +PDZRN3,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +GNG12-AS1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +LDLRAD3,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +TP53I11,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +SLC6A16,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +TAFA4,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +TRHDE-AS1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +CRH,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +RYR3,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +DCHS2,top 50,"Use Persist to select the top 50, 100, and 150 genes", +PTHLH,top 50,"Use Persist to select the top 50, 100, and 150 genes", +GYG2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +KCNK2,top 50,"Use Persist to select the top 50, 100, and 150 genes", +HS3ST2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +IL1RAP,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +TMEM132C,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +SRGAP1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +SULF1,top 50,"Use Persist to select the top 50, 100, and 150 genes", +TRIB2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +COL6A1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +DOCK10,top 50,"Use Persist to select the top 50, 100, and 150 genes", +LHX2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +NXPH1,top 50,"Use Persist to select the top 50, 100, and 150 genes", +SOX6,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +PRELID2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +SFMBT2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +MBP,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", +CDH9,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +PDZRN4,top 50,"Use Persist to select the top 50, 100, and 150 genes", +DKK2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +POSTN,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +CSGALNACT1,top 50,"Use Persist to select the top 50, 100, and 150 genes", +SEMA6D,top 100-150,"Use Persist to select the top 50, 100, and 150 genes", +GRIN3A,top 50-100,"Use Persist to select the top 50, 100, and 150 genes", diff --git a/panel_design/3.csv b/panel_design/3.csv new file mode 100644 index 0000000000000000000000000000000000000000..f2730817a5767dfc85bcf5aa89c8dbb4e69a78d4 --- /dev/null +++ b/panel_design/3.csv @@ -0,0 +1,151 @@ +Gene symbol,Ranking,Annotation & reasoning,Additional note,Data Source +TCL1A,1-50,B cell,,"""Identification and multimodal characterization of a specialized epithelial cell type associated with Crohn’s disease"", CD samples collected from terminal ileum (TI) and ascending colon (AC) through endoscopy and surgical" +MS4A1,1-50,B cell,, +CD79A,1-50,B cell,, +BLK,50-100,B cell,, +FCRL1,50-100,B cell,, +PAX5,50-100,B cell,, +TNFRSF13C,50-100,B cell,, +CNR2,50-100,B cell,, +CD22,50-100,B cell,Mast, +FAM129C,100-150,B cell,, +VPREB3,100-150,B cell,, +BEST4,1-50,BEST4,Canonical marker, +CA7,1-50,BEST4,Canonical marker, +OTOP2,1-50,BEST4,Canonical marker, +CA4,50-100,BEST4,Co-exp with CA7, +NBPF19,50-100,BEST4,, +NBPF14,50-100,BEST4,, +MEIS1,100-150,BEST4,, +ADCY5,100-150,BEST4,, +SPIB,100-150,BEST4,, +AQP8,1-50,Clonocyte,, +CEACAM1,1-50,Clonocyte,, +AQP8,1-50,Colonocyte,, +CA2,50-100,Colonocyte,Multiple cell types, +CA1,50-100,Colonocyte,, +HMGCS2,50-100,Colonocyte,Multiple cell types, +CD24,50-100,Colonocyte,Multiple cell types, +MS4A12,100-150,Colonocyte,, +SLC37A2,100-150,Colonocyte,, +CEACAM7,100-150,Colonocyte,, +SLC26A2,100-150,Colonocyte,, +TOP2A,1-50,Cycling,, +MKI67,1-50,Cycling,, +HMGB2,50-100,Cycling,, +OLFM4,1-50,Cycling/Stem,, +CENPF,50-100,Cycling/Stem,, +PRC1,50-100,Cycling/Stem,, +CCNB2,50-100,Cycling/Stem,, +AURKB,100-150,Cycling/Stem,, +GTSE1,100-150,Cycling/Stem,, +RRM2,100-150,Cycling/Stem,, +CHGB,1-50,EEC,, +CHGA,1-50,EEC,, +PCSK1N,50-100,EEC,, +KCNB2,50-100,EEC,, +NEUROD1,50-100,EEC,, +FEV,100-150,EEC,, +SCG2,100-150,EEC,, +SSTR5-AS1,100-150,EEC,, +ACKR1,1-50,Endo,Vein, +VWF,1-50,Endo,Cannonical endo marker, +PECAM1,1-50,Endo,Canonical marker, +CLDN5,50-100,Endo,, +SOX18,50-100,Endo,, +RAMP3,50-100,Endo,, +RAMP2,50-100,Endo,, +CLEC14A,100-150,Endo,, +TIE1,100-150,Endo,, +APOB,1-50,Enterocyte,Multiple cell types, +APOA4,1-50,Enterocyte,Multiple cell types, +APOA1,1-50,Enterocyte,, +SLC15A1,50-100,Enterocyte,, +SLC6A19,50-100,Enterocyte,, +CYP3A4,100-150,Enterocyte,, +MTTP,100-150,Enterocyte,, +CUBN,100-150,Enterocyte,, +SLC10A2,100-150,Enterocyte,, +SLC7A9,100-150,Enterocyte,, +FABP1,1-50,Epi,Multiple cell types, +COL1A2,1-50,Fibro,"Canonical marker, high expression", +COL1A1,1-50,Fibro,"Canonical marker, high expression", +DCN,1-50,Fibro,"Canonical marker, high expression", +COL3A1,50-100,Fibro,, +PDGFRA,50-100,Fibro,, +MFAP4,50-100,Fibro,, +SFRP2,50-100,Fibro,, +C1R,100-150,Fibro,, +TFF3,1-50,Goblet,"Canonical marker, high expression", +MUC2,1-50,Goblet,"Canonical marker, high expression", +SPINK4,1-50,Goblet,, +ITLN1,50-100,Goblet,, +CLCA1,50-100,Goblet,, +FCGBP,50-100,Goblet,, +BEST2,100-150,Goblet,, +DUOX2,1-50,LND,Important cell state in disease, +LCN2,1-50,LND,Important cell state in disease, +DMBT1,1-50,LND,Important cell state in disease, +REG1A,1-50,LND,Important cell state in disease, +SAA1,50-100,LND,, +NOS2,50-100,LND,, +PI3,100-150,LND,, +PDZK1IP1,100-150,LND,, +CD55,100-150,LND,, +CPA3,1-50,Mast,, +KIT,1-50,Mast,, +CTSG,50-100,Mast,, +GATA2,50-100,Mast,, +TPSAB1,50-100,Mast,, +TPSB2,50-100,Mast,, +MS4A2,100-150,Mast,, +HDC,100-150,Mast,, +C1QA,1-50,Myel,"Canonical myeloid marker, too high expression", +C1QB,1-50,Myel,Canonical myeloid marker, +C1QC,50-100,Myel,Canonical myeloid marker; co-express with C1QA and C1QB, +CSF3R,50-100,Myel,, +FPR1,100-150,Myel,, +MS4A6A,100-150,Myel,, +TYROBP,100-150,Myel,, +AIF1,100-150,Myel,, +MS4A7,100-150,Myel,, +CSF2RA,100-150,Myel,, +S100A8,1-50,Neutrophils,, +S100A9,1-50,Neutrophils,, +NKG7,1-50,NK,, +DEFA6,1-50,Paneth,, +DEFA5,50-100,Paneth,, +ITLN2,100-150,Paneth,, +PLA2G2A,100-150,Paneth,, +CDKN1C,100-150,Paneth,, +IGHA1,1-50,PCs,, +JCHAIN,1-50,PCs,, +IGHA2,1-50,PCs,, +IGKC,50-100,PCs,"Canonical marker, multiple cell types, too high expression", +CCR10,50-100,PCs,, +MZB1,50-100,PCs,, +DERL3,100-150,PCs,, +TNFRSF17,100-150,PCs,, +AC096579.15,100-150,PCs,, +ENAM,100-150,PCs,, +LGR5,1-50,Stem,, +CD3D,1-50,T,, +CD8A,1-50,T,, +TRAC,1-50,T,, +FOXP3,1-50,T,Tregs, +CTLA4,1-50,T,, +GZMB,1-50,T,T-cyto, +CD4,50-100,T,, +CCL5,50-100,T,, +CD3E,50-100,T,, +CD247,100-150,T,, +TRBC1,100-150,T,, +AC092580.4,100-150,T,, +CD96,100-150,T,, +LRMP,1-50,Tuft,, +POU2F3,50-100,Tuft,, +HPGDS,50-100,Tuft,, +SH2D6,100-150,Tuft,, +CCDC129,100-150,Tuft,, +SH2D7,100-150,Tuft,, +PTGS1,100-150,Tuft,, diff --git a/panel_design/4.csv b/panel_design/4.csv new file mode 100644 index 0000000000000000000000000000000000000000..05b0b3704233a8c9c86e2395e1f66632424469b4 --- /dev/null +++ b/panel_design/4.csv @@ -0,0 +1,151 @@ +Unnamed: 0,Gene.Symbol,Ranking,Annotation...reasoning,Additional.note +1,FSTL4,1-50,More distinct marker than L5,markers ranked with cohen mean +2,SATB2,1-50,abundant marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean +3,KCNIP4,1-50,abundant marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean +4,TAFA1,1-50,abundant marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean +5,VAT1L,1-50,abundant marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean +6,CBLN2,1-50,abundant marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean +7,ARPP21,1-50,abundant marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean +8,RAD52,1-50,abundant marker for astrocyte of the cerebral cortex,markers ranked with cohen mean +9,PDK4,1-50,abundant marker for astrocyte of the cerebral cortex,markers ranked with cohen mean +10,SEMA3B,1-50,abundant marker for astrocyte of the cerebral cortex,markers ranked with cohen mean +11,ADARB2,1-50,abundant marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean +12,SORCS3,1-50,abundant marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean +13,CXCL14,1-50,abundant marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean +14,MAD1L1,1-50,abundant marker for cerebral cortex endothelial cell,markers ranked with cohen mean +15,CYP26B1,1-50,abundant marker for cerebral cortex endothelial cell,markers ranked with cohen mean +16,CASP10,1-50,abundant marker for cerebral cortex endothelial cell,markers ranked with cohen mean +17,ZNF536,1-50,abundant marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean +18,ZNF385D,1-50,abundant marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean +19,THSD7A,1-50,abundant marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean +20,SEMA3E,1-50,abundant marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean +21,EGFEM1P,1-50,abundant marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean +22,LAMP5,1-50,abundant marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean +23,FGF13,1-50,abundant marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean +24,C1orf112,1-50,abundant marker for microglial cell,markers ranked with cohen mean +25,CEACAM21,1-50,abundant marker for microglial cell,markers ranked with cohen mean +26,TYROBP,1-50,abundant marker for microglial cell,markers ranked with cohen mean +27,TSHZ2,1-50,abundant marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean +28,HTR2C,1-50,abundant marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean +29,GCFC2,1-50,abundant marker for oligodendrocyte,markers ranked with cohen mean +30,LAMP2,1-50,abundant marker for oligodendrocyte,markers ranked with cohen mean +31,TMEM98,1-50,abundant marker for oligodendrocyte,markers ranked with cohen mean +32,HECW1,1-50,abundant marker for oligodendrocyte precursor cell,markers ranked with cohen mean +33,KLHL13,1-50,abundant marker for oligodendrocyte precursor cell,markers ranked with cohen mean +34,ATP1A2,1-50,abundant marker for oligodendrocyte precursor cell,markers ranked with cohen mean +35,ABTB3,1-50,abundant marker for pvalb GABAergic cortical interneuron,markers ranked with cohen mean +36,GCLC,1-50,abundant marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean +37,HCCS,1-50,abundant marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean +38,DPEP1,1-50,abundant marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean +39,SST,1-50,abundant marker for sst GABAergic cortical interneuron,markers ranked with cohen mean +40,GRIK1,1-50,abundant marker for sst GABAergic cortical interneuron,markers ranked with cohen mean +41,SYNPR,1-50,abundant marker for sst GABAergic cortical interneuron,markers ranked with cohen mean +42,ATP1A2,1-50,abundant marker for vascular leptomeningeal cell,markers ranked with cohen mean +43,EBF1,1-50,abundant marker for vascular leptomeningeal cell,markers ranked with cohen mean +44,PDGFRB,1-50,abundant marker for vascular leptomeningeal cell,markers ranked with cohen mean +45,VIP,1-50,abundant marker for vip GABAergic cortical interneuron,markers ranked with cohen mean +46,GALNTL6,1-50,abundant marker for vip GABAergic cortical interneuron,markers ranked with cohen mean +47,CX3CR1,1-50,abundant marker for microglial cell,Known Marker +48,DLGAP2,50-100,Less specific marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean +49,STXBP5L,50-100,Less specific marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean +50,CHRM3,50-100,Less specific marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean +51,NRGN,50-100,Less specific marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean +52,PDE1A,50-100,Less specific marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean +53,RALYL,50-100,Less specific marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean +54,PTPRR,50-100,Less specific marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean +55,MARCHF1,50-100,Less specific marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean +56,NKX2-2,50-100,Less specific marker for astrocyte of the cerebral cortex,markers ranked with cohen mean +57,OBI1-AS1,50-100,Less specific marker for astrocyte of the cerebral cortex,markers ranked with cohen mean +58,CRACD,50-100,Less specific marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean +59,MYO16,50-100,Less specific marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean +60,CACNA1B,50-100,Less specific marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean +61,ID3,50-100,Less specific marker for cerebral cortex endothelial cell,markers ranked with cohen mean +62,TBX3,50-100,Less specific marker for cerebral cortex endothelial cell,markers ranked with cohen mean +63,PLXND1,50-100,Less specific marker for cerebral cortex endothelial cell,markers ranked with cohen mean +64,TMEM132D,50-100,Less specific marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean +65,TENM1,50-100,Less specific marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean +66,SDK1,50-100,Less specific marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean +67,CLSTN2,50-100,Less specific marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean +68,RYR2,50-100,Less specific marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean +69,NRG1,50-100,Less specific marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean +70,NYAP2,50-100,Less specific marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean +71,MTUS2,50-100,Less specific marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean +72,LINC00299,50-100,Less specific marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean +73,APBB1IP,50-100,Less specific marker for microglial cell,markers ranked with cohen mean +74,SH3BP2,50-100,Less specific marker for microglial cell,markers ranked with cohen mean +75,C1QC,50-100,Less specific marker for microglial cell,markers ranked with cohen mean +76,FOXP2,50-100,Less specific marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean +77,CHN2,50-100,Less specific marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean +78,MED24,50-100,Less specific marker for oligodendrocyte,markers ranked with cohen mean +79,DAPK2,50-100,Less specific marker for oligodendrocyte,markers ranked with cohen mean +80,BCAS1,50-100,Less specific marker for oligodendrocyte,markers ranked with cohen mean +81,CTNS,50-100,Less specific marker for oligodendrocyte precursor cell,markers ranked with cohen mean +82,BCAS1,50-100,Less specific marker for oligodendrocyte precursor cell,markers ranked with cohen mean +83,SOX6,50-100,Less specific marker for oligodendrocyte precursor cell,markers ranked with cohen mean +84,ADAMTS17,50-100,Less specific marker for pvalb GABAergic cortical interneuron,markers ranked with cohen mean +85,FGF12,50-100,Less specific marker for pvalb GABAergic cortical interneuron,markers ranked with cohen mean +86,GRIP1,50-100,Less specific marker for pvalb GABAergic cortical interneuron,markers ranked with cohen mean +87,KMO,50-100,Less specific marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean +88,KCNK17,50-100,Less specific marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean +89,STXBP6,50-100,Less specific marker for sst GABAergic cortical interneuron,markers ranked with cohen mean +90,CDH9,50-100,Less specific marker for sst GABAergic cortical interneuron,markers ranked with cohen mean +91,ELAVL2,50-100,Less specific marker for sst GABAergic cortical interneuron,markers ranked with cohen mean +92,UTRN,50-100,Less specific marker for vascular leptomeningeal cell,markers ranked with cohen mean +93,CALD1,50-100,Less specific marker for vascular leptomeningeal cell,markers ranked with cohen mean +94,LAMA2,50-100,Less specific marker for vascular leptomeningeal cell,markers ranked with cohen mean +95,GALNT13,50-100,Less specific marker for vip GABAergic cortical interneuron,markers ranked with cohen mean +96,SNTG1,50-100,Less specific marker for vip GABAergic cortical interneuron,markers ranked with cohen mean +97,LINC01480,100-150,de-enriched marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean +98,AIF1,100-150,de-enriched marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean +99,MGC16275,100-150,de-enriched marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean +100,SALL3,100-150,de-enriched marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean +101,FMO6P,100-150,de-enriched marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean +102,GPRC5B,100-150,de-enriched marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean +103,SEMA6A,100-150,de-enriched marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean +104,CAPN2,100-150,de-enriched marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean +105,IL1RAPL1,100-150,de-enriched marker for astrocyte of the cerebral cortex,markers ranked with cohen mean +106,DSCAM,100-150,de-enriched marker for astrocyte of the cerebral cortex,markers ranked with cohen mean +107,PPP1R13L,100-150,de-enriched marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean +108,INPPL1,100-150,de-enriched marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean +109,EXPH5,100-150,de-enriched marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean +110,NCAM1,100-150,de-enriched marker for cerebral cortex endothelial cell,markers ranked with cohen mean +111,GABRG3,100-150,de-enriched marker for cerebral cortex endothelial cell,markers ranked with cohen mean +112,VRK2,100-150,de-enriched marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean +113,TRPM3,100-150,de-enriched marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean +114,CSGALNACT1,100-150,de-enriched marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean +115,RND3,100-150,de-enriched marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean +116,NOTCH2NLA,100-150,de-enriched marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean +117,EGFR,100-150,de-enriched marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean +118,DKKL1,100-150,de-enriched marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean +119,TNFSF10,100-150,de-enriched marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean +120,TRIB1,100-150,de-enriched marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean +121,DOK6,100-150,de-enriched marker for microglial cell,markers ranked with cohen mean +122,METTL6,100-150,de-enriched marker for microglial cell,markers ranked with cohen mean +123,TRIM16,100-150,de-enriched marker for microglial cell,markers ranked with cohen mean +124,HMOX1,100-150,de-enriched marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean +125,ETS1,100-150,de-enriched marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean +126,HERC2P4,100-150,de-enriched marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean +127,MYO1F,100-150,de-enriched marker for oligodendrocyte,markers ranked with cohen mean +128,GASK1B,100-150,de-enriched marker for oligodendrocyte,markers ranked with cohen mean +129,RTCB,100-150,de-enriched marker for oligodendrocyte,markers ranked with cohen mean +130,RBFOX3,100-150,de-enriched marker for oligodendrocyte precursor cell,markers ranked with cohen mean +131,TMEM119,100-150,de-enriched marker for pvalb GABAergic cortical interneuron,markers ranked with cohen mean +132,CAVIN2,100-150,de-enriched marker for pvalb GABAergic cortical interneuron,markers ranked with cohen mean +133,GBGT1,100-150,de-enriched marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean +134,IL6ST,100-150,de-enriched marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean +135,SFMBT2,100-150,de-enriched marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean +136,HS3ST6,100-150,de-enriched marker for sst GABAergic cortical interneuron,markers ranked with cohen mean +137,EPHA2,100-150,de-enriched marker for sst GABAergic cortical interneuron,markers ranked with cohen mean +138,CHST3,100-150,de-enriched marker for sst GABAergic cortical interneuron,markers ranked with cohen mean +139,CNIH3,100-150,de-enriched marker for vascular leptomeningeal cell,markers ranked with cohen mean +140,AGTPBP1,100-150,de-enriched marker for vascular leptomeningeal cell,markers ranked with cohen mean +141,AFDN,100-150,de-enriched marker for vascular leptomeningeal cell,markers ranked with cohen mean +142,MOBP,100-150,de-enriched marker for vip GABAergic cortical interneuron,markers ranked with cohen mean +143,LINC01094,100-150,de-enriched marker for vip GABAergic cortical interneuron,markers ranked with cohen mean +144,SAMD9L,100-150,de-enriched marker for vip GABAergic cortical interneuron,markers ranked with cohen mean +145,ALDH1L1,1-50,known marker gene for astrocyte of the cerebral cortex,sourced from prior knowledge +146,MBP,50-100,known marker gene for oligodendrocyte,sourced from prior knowledge +147,GFAP,50-100,known marker gene for astrocyte,sourced from prior knowledge +148,AQP4,1-50,known marker gene for astrocyte,sourced from prior knowledge +149,PVALB,50-100,spcific marker for pvalb interneurons,sourced from prior knowledge +150,SST,1-50,known marker gene SST interneurons,sourced from prior knowledge diff --git a/panel_design/5.csv b/panel_design/5.csv new file mode 100644 index 0000000000000000000000000000000000000000..941549341fbc49deb6d13f902c8a4e418a595cf3 --- /dev/null +++ b/panel_design/5.csv @@ -0,0 +1,151 @@ +Gene symbol,Ranking,Annotation & Reasoning +ADARB2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +ERBB4,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +ROBO2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +KCNIP4,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +DPP10,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +SGCZ,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +PLP1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +DCC,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +CNTN5,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +LINGO2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +PCDH9,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +KCNMB2-AS1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +PTPRT,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +HS3ST4,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +PCDH9-AS2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +GALNTL6,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +CDH12,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +RELN,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +CCK,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +GRID2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +NTM,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +CLDN5,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +LRP1B,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +FTH1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +ROBO1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +PRKG1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +GPC6,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +MGAT4C,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +NLGN1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +CDH13,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +ZNF804B,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +NKAIN2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +BCYRN1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +NRG1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +LRRTM4,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +NCAM2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +PDE5A,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +TSHZ2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +ARHGAP24,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +PCDH7,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +LINC00609,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +HS6ST3,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +TAFA2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +SLC8A1-AS1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +PDE4B,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +TRPM3,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +PDE1A,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +SOX5,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +GRIK1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +GAPDH,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +EPHA6,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +PEX5L,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +PLXDC2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +KIRREL3,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +UNC5D,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +CXCL14,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +FTL,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +MARCHF1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +CTNNA2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +ASIC2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +LAMA2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +PCDH11Y,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +SORCS3,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +SRGAP2-AS1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +KAZN,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +NPAS3,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +TOX,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +HFM1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +ALCAM,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +SDK1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +PPARGC1A,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +SLC6A1-AS1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +CDH20,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +SLC5A11,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +NELL1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +DPP6,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +RPS27A,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +ITPR2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +ATP6V0C,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +ZBTB20,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +DPP10-AS3,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +CNTNAP2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +INPP4B,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +MOBP,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +NTNG1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +GPC5,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +PTPRK,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +KCNH7,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +SLIT2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +PCSK1N,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +UNC5C,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +APBB1IP,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +RALYL,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +LRRC4C,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +SPOCK3,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +SGCD,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +ASTN2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +SST,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +NRXN1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +NRGN,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +DOCK8,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +GRM3,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +LRRTM3,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +KCNQ5,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +VIP,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +UBE3A,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +RAPGEF5,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +CNTN4,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +GLIS3,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +RPL26,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +NCKAP5,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +GRIA4,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +LEF1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +TMTC2,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +RGS6,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +DPYD,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +PLCL1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +TUBB2A,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +SOX2-OT,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +PDE1C,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +QKI,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +EDIL3,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +TAFA1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +SYT1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +MAML2,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +SLC8A1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +TENM2,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +DSCAML1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +BCAS1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +FAM177B,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +CSGALNACT1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +ARHGAP26,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +ATRNL1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +EEF1A1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +CNTNAP4,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +ST18,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +HPSE2,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +DLC1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +IL1RAPL1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +ZNF536,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +CHST11,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +DAB1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +CALM1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +DGKB,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +ST6GALNAC3,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +KCNQ3,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +DSCAM,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +SYNJ2,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +FHIT,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data +SAMSN1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data diff --git a/panel_design/6.csv b/panel_design/6.csv new file mode 100644 index 0000000000000000000000000000000000000000..dd86ad22a38e0afec9606f4c9f061edfa6218591 --- /dev/null +++ b/panel_design/6.csv @@ -0,0 +1,151 @@ +symbol,Ranking,Annotation & Reasoning,gene,cellType.target,mean.target,cellType,mean,ratio,rank_ratio,anno_ratio,logFC,log.p.value,log.FDR,std.logFC,rank_marker,anno_logFC,Unnamed: 17,cellTypeResolution +BTBD11,1,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000151136,Inhib,2.607133345,OPC,0.3050867244,8.545548319,7,Inhib/OPC: 8.546,2.221299082,-22165.77242,-22155.57679,2.991557876,1, std logFC = 2.992,,broad +ST18,2,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000147488,Oligo,4.651734362,Micro,0.3638996875,12.78301279,11,Oligo/Micro: 12.783,4.465531379,-38170.35,-38160.15437,4.392440029,1, std logFC = 4.392,,broad +AC004852.2,3,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000278254,OPC,3.198955525,Inhib,0.06788469054,47.12337199,1,OPC/Inhib: 47.123,3.162152196,-34123.87078,-34113.67514,8.5255685,1, std logFC = 8.526,,broad +OBI1-AS1,4,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000234377,Astro,3.758269422,Oligo,0.3138484541,11.97479029,2,Astro/Oligo: 11.975,3.571921082,-22878.94594,-22868.75031,4.389697553,1, std logFC = 4.39,,broad +ITIH5,5,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000123243,EndoMural,3.034600494,Micro,0.0843586809,35.972593,2,EndoMural/Micro: 35.973,2.979076489,-24405.49833,-24395.3027,6.140134848,1, std logFC = 6.14,,broad +DOCK8,6,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000107099,Micro,3.89732526,EndoMural,0.2363190559,16.4917943,19,Micro/EndoMural: 16.492,3.849979809,-33550.52388,-33540.32824,9.123545355,1, std logFC = 9.124,,broad +BTBD11,7,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000151136,Inhib,2.607133345,Excit_L2/3,0.4583811315,5.687697783,4,Inhib/Excit_L2/3: 5.688,2.232219442,-21879.15743,-21868.96179,3.009130469,1, std logFC = 3.009,,layer +ST18,8,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000147488,Oligo,4.651734362,Micro,0.3638996875,12.78301279,11,Oligo/Micro: 12.783,4.498617988,-37842.74672,-37832.55109,4.45769983,1, std logFC = 4.458,,layer +AC004852.2,9,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000278254,OPC,3.198955525,Inhib,0.06788469054,47.12337199,1,OPC/Inhib: 47.123,3.161932798,-33191.99065,-33181.79502,8.447310226,1, std logFC = 8.447,,layer +MAP1B,10,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000131711,Excit_L3/4/5,5.481322199,Excit_L4,4.859234787,1.128021682,25,Excit_L3/4/5/Excit_L4: 1.128,2.357513634,-3728.573791,-3718.378156,1.697613701,1, std logFC = 1.698,,layer +CBLN2,11,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000141668,Excit_L3,2.326846695,Excit_L5/6,1.692531181,1.37477331,21,Excit_L3/Excit_L5/6: 1.375,1.884852238,-12389.93168,-12379.73605,1.969356146,1, std logFC = 1.969,,layer +OBI1-AS1,12,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000234377,Astro,3.758269422,Oligo,0.3138484541,11.97479029,2,Astro/Oligo: 11.975,3.59279821,-24845.60054,-24835.4049,4.724479205,1, std logFC = 4.724,,layer +ITIH5,13,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000123243,EndoMural,3.034600494,Excit_L3/4/5,0.1202223609,25.24156465,3,EndoMural/Excit_L3/4/5: 25.242,2.982326922,-24223.80788,-24213.61225,6.170504958,1, std logFC = 6.171,,layer +DOCK8,14,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000107099,Micro,3.89732526,EndoMural,0.2363190559,16.4917943,17,Micro/EndoMural: 16.492,3.849829499,-32837.07734,-32826.8817,9.082881361,1, std logFC = 9.083,,layer +MCTP2,15,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000140563,Excit_L6,1.145245232,EndoMural,0.1418083509,8.076006977,2,Excit_L6/EndoMural: 8.076,1.102705535,-6974.182921,-6963.987287,3.03953067,1, std logFC = 3.04,,layer +THEMIS,16,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000172673,Excit_L5/6,1.180947014,Excit_L5,0.4727839547,2.497857641,2,Excit_L5/6/Excit_L5: 2.498,1.046802894,-4183.521725,-4173.326091,1.965745525,1, std logFC = 1.966,,layer +AP003066.1,17,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000254587,Excit_L5,0.9709158406,Excit_L4,0.291616737,3.329424266,1,Excit_L5/Excit_L4: 3.329,0.9097670434,-7118.396732,-7108.201098,2.6322311,1, std logFC = 2.632,,layer +GAD2,18,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000136750,Inhib,2.326517424,Oligo,0.1658070258,14.03147673,3,Inhib/Oligo: 14.031,2.115583238,-20977.67585,-20968.17336,2.875964071,2, std logFC = 2.876,,broad +PDGFRA,19,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000134853,OPC,2.708879833,EndoMural,0.1486911615,18.21816311,4,OPC/EndoMural: 18.218,2.660844387,-24926.02107,-24916.51858,6.623062703,2, std logFC = 6.623,,broad +CABP1,20,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000157782,Excit,2.510583463,Oligo,0.504915941,4.972280055,21,Excit/Oligo: 4.972,1.913232828,-17212.32586,-17202.82338,1.918615179,2, std logFC = 1.919,,broad +ADGRV1,21,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164199,Astro,4.473246084,Excit,0.5158270259,8.671988591,6,Astro/Excit: 8.672,3.978323448,-20881.17828,-20871.67579,4.110730183,2, std logFC = 4.111,,broad +EBF1,22,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164330,EndoMural,3.366858494,Inhib,0.1538390138,21.88559593,7,EndoMural/Inhib: 21.886,3.28545377,-19807.44179,-19797.9393,5.282737171,2, std logFC = 5.283,,broad +APBB1IP,23,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000077420,Micro,3.82802585,EndoMural,0.1379134818,27.75671966,12,Micro/EndoMural: 27.757,3.785317824,-33045.61491,-33036.11242,9.006461122,2, std logFC = 9.006,,broad +GAD2,24,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000136750,Inhib,2.326517424,Excit_L2/3,0.6869905244,3.386534954,9,Inhib/Excit_L2/3: 3.387,2.117471009,-21035.58962,-21026.08713,2.924786644,2, std logFC = 2.925,,layer +PDGFRA,25,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000134853,OPC,2.708879833,EndoMural,0.1486911615,18.21816311,4,OPC/EndoMural: 18.218,2.661366083,-24639.22108,-24629.7186,6.636112056,2, std logFC = 6.636,,layer +CALM1,26,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198668,Excit_L3/4/5,5.74028179,Excit_L4,4.987590797,1.15091274,15,Excit_L3/4/5/Excit_L4: 1.151,2.354909866,-3489.442816,-3479.940328,1.638514659,2, std logFC = 1.639,,layer +CUX2,27,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000111249,Excit_L3,2.400736547,Inhib,1.535578791,1.563408248,7,Excit_L3/Inhib: 1.563,1.969430629,-12347.48171,-12337.97923,1.965153047,2, std logFC = 1.965,,layer +ADGRV1,28,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164199,Astro,4.473246084,Excit_L3,0.5998035463,7.457852011,6,Astro/Excit_L3: 7.458,3.997336738,-20967.08141,-20957.57892,4.167294033,2, std logFC = 4.167,,layer +EBF1,29,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164330,EndoMural,3.366858494,Inhib,0.1538390138,21.88559593,5,EndoMural/Inhib: 21.886,3.297611891,-21376.38612,-21366.88363,5.626266372,2, std logFC = 5.626,,layer +APBB1IP,30,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000077420,Micro,3.82802585,EndoMural,0.1379134818,27.75671966,11,Micro/EndoMural: 27.757,3.786556774,-32457.1598,-32447.65732,8.992548136,2, std logFC = 8.993,,layer +AC099517.1,31,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000287176,Excit_L5/6,1.096716549,Excit_L5,0.7892758353,1.389522522,22,Excit_L5/6/Excit_L5: 1.39,0.9844434124,-4108.21197,-4098.709483,1.94656857,2, std logFC = 1.947,,layer +AC073091.3,32,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000287172,Excit_L5,2.799017012,Excit_L5/6,1.732461721,1.615629932,14,Excit_L5/Excit_L5/6: 1.616,2.240268387,-5465.523097,-5456.02061,2.270115933,2, std logFC = 2.27,,layer +MOBP,33,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000168314,Oligo,3.538881555,Micro,0.3821774358,9.259786749,19,Oligo/Micro: 9.26,3.2201278,-27951.86856,-27942.77154,3.37455489,3, std logFC = 3.375,,broad +MEGF11,34,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000157890,OPC,3.351998122,Inhib,0.2164735628,15.48456116,5,OPC/Inhib: 15.485,3.22097198,-24488.41936,-24479.32234,6.535601574,3, std logFC = 6.536,,broad +ADAM28,35,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000042980,Micro,3.005809501,Inhib,0.1116458658,26.92271209,14,Micro/Inhib: 26.923,2.953040163,-26207.24442,-26198.1474,7.470789811,3, std logFC = 7.471,,broad +GAD1,36,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000128683,Inhib,2.370257819,OPC,0.9781879376,2.423110864,20,Inhib/OPC: 2.423,2.134891009,-20843.6985,-20834.60148,2.905628895,3, std logFC = 2.906,,layer +MOBP,37,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000168314,Oligo,3.538881555,Excit_L2/3,0.429038715,8.248396779,19,Oligo/Excit_L2/3: 8.248,3.264762216,-28589.15327,-28580.05624,3.498833224,3, std logFC = 3.499,,layer +MEGF11,38,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000157890,OPC,3.351998122,Excit_L6,0.4393208706,7.6299542,8,OPC/Excit_L6: 7.63,3.221269673,-24076.45133,-24067.35431,6.521045022,3, std logFC = 6.521,,layer +TUBA1B,39,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000123416,Excit_L3/4/5,3.856794784,Excit_L4,3.259121796,1.183384674,10,Excit_L3/4/5/Excit_L4: 1.183,2.152929616,-3376.611792,-3367.51477,1.610062965,3, std logFC = 1.61,,layer +TSHZ2,40,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000182463,Excit_L4,2.513501671,Excit_L5,1.815381111,1.384558678,1,Excit_L4/Excit_L5: 1.385,2.041678543,-3525.994817,-3516.897794,1.825827726,3, std logFC = 1.826,,layer +AL137139.2,41,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000286757,Astro,2.750399175,EndoMural,0.7166476389,3.837868188,20,Astro/EndoMural: 3.838,2.595534945,-17585.73933,-17576.64231,3.686137516,3, std logFC = 3.686,,layer +EPAS1,42,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000116016,EndoMural,3.286727061,OPC,0.583780088,5.630077368,24,EndoMural/OPC: 5.63,3.13899098,-18569.36355,-18560.26653,5.094193063,3, std logFC = 5.094,,layer +ADAM28,43,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000042980,Micro,3.005809501,Inhib,0.1116458658,26.92271209,13,Micro/Inhib: 26.923,2.953090913,-25692.73878,-25683.64175,7.436880268,3, std logFC = 7.437,,layer +LINC00343,44,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000226620,Excit_L5/6,0.6838777434,Excit_L4,0.1966485482,3.477664848,1,Excit_L5/6/Excit_L4: 3.478,0.612425043,-3815.624154,-3806.527131,1.870723949,3, std logFC = 1.871,,layer +AL033539.2,45,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000286954,Excit_L5,0.5831326126,Excit_L4,0.2623003828,2.223148157,4,Excit_L5/Excit_L4: 2.223,0.5387135463,-5367.091019,-5357.993996,2.247459805,3, std logFC = 2.247,,layer +GRIP2,46,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000144596,Inhib,1.454320477,EndoMural,0.1302827841,11.16279857,6,Inhib/EndoMural: 11.163,1.294597378,-18824.6624,-18815.85306,2.666933752,4, std logFC = 2.667,,broad +BX284613.2,47,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000231424,OPC,3.206382317,EndoMural,0.496363375,6.459747995,13,OPC/EndoMural: 6.46,3.113238963,-23593.02996,-23584.22062,6.357212581,4, std logFC = 6.357,,broad +LINC00299,48,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000236790,Astro,3.197232057,Excit,0.4970508863,6.432403895,11,Astro/Excit: 6.432,2.843051279,-15690.46465,-15681.65531,3.386312678,4, std logFC = 3.386,,broad +FLT1,49,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000102755,EndoMural,3.250079224,Micro,0.2425572587,13.39922475,12,EndoMural/Micro: 13.399,3.128544555,-15945.16946,-15936.36012,4.563792751,4, std logFC = 4.564,,broad +TBXAS1,50,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000059377,Micro,2.968864785,Astro,0.1011493705,29.35129273,10,Micro/Astro: 29.351,2.920644873,-24296.42317,-24287.61383,7.054872707,4, std logFC = 7.055,,broad +ZNF385D,51,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000151789,Inhib,3.711723082,Excit_L2/3,1.636083675,2.268663357,23,Inhib/Excit_L2/3: 2.269,3.094519038,-19109.90017,-19101.09083,2.732838004,4, std logFC = 2.733,,layer +VCAN,52,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000038427,OPC,4.239971898,Astro,0.6973526945,6.080096817,14,OPC/Astro: 6.08,4.093962541,-23078.97859,-23070.16925,6.317945452,4, std logFC = 6.318,,layer +STMN2,53,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000104435,Excit_L3/4/5,3.558073538,Excit_L4,3.151266501,1.129093187,24,Excit_L3/4/5/Excit_L4: 1.129,2.066072579,-2987.657537,-2978.848197,1.508844291,4, std logFC = 1.509,,layer +FLT1,54,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000102755,EndoMural,3.250079224,Micro,0.2425572587,13.39922475,10,EndoMural/Micro: 13.399,3.135745883,-15914.36183,-15905.55249,4.590363107,4, std logFC = 4.59,,layer +TBXAS1,55,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000059377,Micro,2.968864785,Astro,0.1011493705,29.35129273,9,Micro/Astro: 29.351,2.920646171,-23800.559,-23791.74966,7.016004295,4, std logFC = 7.016,,layer +AC019211.1,56,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000239498,Excit_L5/6,2.768667305,Excit_L3,1.951978314,1.418390402,19,Excit_L5/6/Excit_L3: 1.418,1.936861229,-3285.072502,-3276.263162,1.72699297,4, std logFC = 1.727,,layer +TLL1,57,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000038295,Excit_L5,1.566350741,Excit_L5/6,0.7142612438,2.192966165,5,Excit_L5/Excit_L5/6: 2.193,1.413459313,-5326.778106,-5317.968765,2.238138652,4, std logFC = 2.238,,layer +TF,58,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000091513,Oligo,3.281925193,Astro,0.4361364794,7.524995838,22,Oligo/Astro: 7.525,2.974140605,-25258.08742,-25249.50122,3.120593098,5, std logFC = 3.121,,broad +VCAN,59,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000038427,OPC,4.239971898,Astro,0.6973526945,6.080096817,15,OPC/Astro: 6.08,4.093854266,-23366.01208,-23357.42589,6.312093879,5, std logFC = 6.312,,broad +PRDM16,60,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000142611,Astro,1.965483537,EndoMural,0.1415356302,13.88684626,1,Astro/EndoMural: 13.887,1.890689945,-15153.31748,-15144.73129,3.31059695,5, std logFC = 3.311,,broad +COBLL1,61,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000082438,EndoMural,3.535001711,Excit,0.3801767269,9.29831171,17,EndoMural/Excit: 9.298,3.273943496,-15446.89134,-15438.30514,4.470288074,5, std logFC = 4.47,,broad +CSF2RA,62,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198223,Micro,2.489448328,EndoMural,0.03987363654,62.43344085,5,Micro/EndoMural: 62.433,2.464231696,-23546.26297,-23537.67677,6.892680072,5, std logFC = 6.893,,broad +GRIP2,63,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000144596,Inhib,1.454320477,Excit_L2/3,0.41529399,3.501905907,8,Inhib/Excit_L2/3: 3.502,1.297473899,-18670.68231,-18662.09611,2.689107384,5, std logFC = 2.689,,layer +TF,64,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000091513,Oligo,3.281925193,Astro,0.4361364794,7.524995838,21,Oligo/Astro: 7.525,3.031333133,-26430.26489,-26421.6787,3.287157931,5, std logFC = 3.287,,layer +BX284613.2,65,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000231424,OPC,3.206382317,EndoMural,0.496363375,6.459747995,12,OPC/EndoMural: 6.46,3.111097484,-22884.31923,-22875.73304,6.278424716,5, std logFC = 6.278,,layer +CALM3,66,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000160014,Excit_L3/4/5,3.709015615,Excit_L4,3.223571557,1.150591991,16,Excit_L3/4/5/Excit_L4: 1.151,1.985257658,-2909.569992,-2900.983795,1.487876047,5, std logFC = 1.488,,layer +AC092957.1,67,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000243620,EndoMural,2.16355825,Astro,0.1154847537,18.73457908,6,EndoMural/Astro: 18.735,2.136118087,-15352.65638,-15344.07018,4.48316734,5, std logFC = 4.483,,layer +CSF2RA,68,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198223,Micro,2.489448328,Excit_L2/3,0.04203762655,59.21952623,5,Micro/Excit_L2/3: 59.22,2.464479819,-23024.7494,-23016.1632,6.844712531,5, std logFC = 6.845,,layer +LINC02718,69,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000255418,Excit_L6,0.6996451567,EndoMural,0.06321257245,11.06813296,1,Excit_L6/EndoMural: 11.068,0.6548837902,-4351.073982,-4342.487785,2.341138402,5, std logFC = 2.341,,layer +CASC15,70,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000272168,Excit_L5,3.445054998,Excit_L4,2.00686097,1.716638596,13,Excit_L5/Excit_L4: 1.717,2.561174412,-5282.18967,-5273.603473,2.227799686,5, std logFC = 2.228,,layer +ENPP2,71,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000136960,Oligo,2.833830413,OPC,0.2844518545,9.962425514,15,Oligo/OPC: 9.962,2.707451245,-24995.76515,-24987.36127,3.096082136,6, std logFC = 3.096,,broad +LHFPL3,72,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000187416,OPC,5.943482667,Inhib,0.8290598316,7.168942988,10,OPC/Inhib: 7.169,5.602913607,-20162.95467,-20154.5508,5.678810335,6, std logFC = 5.679,,broad +AC092957.1,73,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000243620,EndoMural,2.16355825,Astro,0.1154847537,18.73457908,8,EndoMural/Astro: 18.735,2.134194384,-15379.8881,-15371.48422,4.457692082,6, std logFC = 4.458,,broad +FYB1,74,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000082074,Micro,2.551032802,EndoMural,0.1481983393,17.2136396,18,Micro/EndoMural: 17.214,2.523305139,-21189.15714,-21180.75326,6.385918489,6, std logFC = 6.386,,broad +LHFPL3,75,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000187416,OPC,5.943482667,Inhib,0.8290598316,7.168942988,9,OPC/Inhib: 7.169,5.625539581,-20159.08824,-20150.68437,5.728066973,6, std logFC = 5.728,,layer +NORAD,76,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000260032,Excit_L3/4/5,3.350699442,Excit_L4,2.841384123,1.17924902,11,Excit_L3/4/5/Excit_L4: 1.179,1.867739455,-2904.971247,-2896.567372,1.486633854,6, std logFC = 1.487,,layer +AC008574.1,77,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000251293,Excit_L3,0.9575423718,Excit_L2/3,0.4575828704,2.092609741,2,Excit_L3/Excit_L2/3: 2.093,0.8900419455,-10798.82644,-10790.42256,1.809927618,6, std logFC = 1.81,,layer +PRDM16,78,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000142611,Astro,1.965483537,EndoMural,0.1415356302,13.88684626,1,Astro/EndoMural: 13.887,1.893996413,-14914.85085,-14906.44697,3.304215312,6, std logFC = 3.304,,layer +FYB1,79,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000082074,Micro,2.551032802,EndoMural,0.1481983393,17.2136396,16,Micro/EndoMural: 17.214,2.523447889,-20643.88812,-20635.48424,6.322406089,6, std logFC = 6.322,,layer +ADAMTSL1,80,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000178031,Excit_L6,2.377115062,Excit_L5,0.9454280519,2.514326773,6,Excit_L6/Excit_L5: 2.514,2.008588364,-4203.428163,-4195.024288,2.297825729,6, std logFC = 2.298,,layer +ANK1,81,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000029534,Inhib,1.640331625,Excit,0.2162203098,7.586390134,10,Inhib/Excit: 7.586,1.474123431,-16081.11571,-16072.86598,2.399709636,7, std logFC = 2.4,,broad +FERMT1,82,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000101311,OPC,1.722814414,EndoMural,0.04082874593,42.1961139,2,OPC/EndoMural: 42.196,1.692500655,-18984.97429,-18976.72456,5.446649042,7, std logFC = 5.447,,broad +MLIP,83,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000146147,Excit,1.646129521,Oligo,0.2236439769,7.360491186,10,Excit/Oligo: 7.36,1.419368353,-12476.19844,-12467.94872,1.560524131,7, std logFC = 1.561,,broad +GLI3,84,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000106571,Astro,1.974149301,EndoMural,0.407848692,4.840396305,18,Astro/EndoMural: 4.84,1.886689057,-14289.69413,-14281.44441,3.18822336,7, std logFC = 3.188,,broad +ATP10A,85,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000206190,EndoMural,2.90391912,Excit,0.1122570077,25.86848855,5,EndoMural/Excit: 25.868,2.803930781,-15174.06071,-15165.81099,4.418962316,7, std logFC = 4.419,,broad +ANK1,86,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000029534,Inhib,1.640331625,Excit_L2/3,0.6175970678,2.655989983,14,Inhib/Excit_L2/3: 2.656,1.50843475,-17034.06009,-17025.81037,2.526017253,7, std logFC = 2.526,,layer +ENPP2,87,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000136960,Oligo,2.833830413,OPC,0.2844518545,9.962425514,14,Oligo/OPC: 9.962,2.734614411,-25019.02205,-25010.77233,3.150591195,7, std logFC = 3.151,,layer +COL9A1,88,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000112280,OPC,1.788626477,EndoMural,0.09293992573,19.24497425,3,OPC/EndoMural: 19.245,1.762865747,-18536.14412,-18527.8944,5.401756121,7, std logFC = 5.402,,layer +GLI3,89,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000106571,Astro,1.974149301,EndoMural,0.407848692,4.840396305,13,Astro/EndoMural: 4.84,1.886750458,-13995.79917,-13987.54944,3.171460728,7, std logFC = 3.171,,layer +ATP10A,90,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000206190,EndoMural,2.90391912,Excit_L6,0.2181010003,13.31456122,11,EndoMural/Excit_L6: 13.315,2.806470362,-14997.12634,-14988.87662,4.415139852,7, std logFC = 4.415,,layer +C3,91,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000125730,Micro,2.52206604,Oligo,0.07219728309,34.93297714,7,Micro/Oligo: 34.933,2.485800545,-20240.83407,-20232.58435,6.234341526,7, std logFC = 6.234,,layer +COL9A1,92,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000112280,OPC,1.788626477,EndoMural,0.09293992573,19.24497425,3,OPC/EndoMural: 19.245,1.762132666,-18731.56562,-18723.44942,5.396693614,8, std logFC = 5.397,,broad +CARMN,93,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000249669,EndoMural,1.643565406,Oligo,0.0263563217,62.35943788,1,EndoMural/Oligo: 62.359,1.627501258,-14393.44504,-14385.32884,4.271525569,8, std logFC = 4.272,,broad +LINC01374,94,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000280560,Micro,2.665601597,Inhib,0.08852780987,30.11033031,9,Micro/Inhib: 30.11,2.614257162,-19845.10532,-19836.98913,6.098132232,8, std logFC = 6.098,,broad +TMEM144,95,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164124,Oligo,3.011743854,Astro,0.5404121759,5.573049588,24,Oligo/Astro: 5.573,2.813875241,-23039.42418,-23031.30798,2.9609775,8, std logFC = 2.961,,layer +FERMT1,96,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000101311,OPC,1.722814414,Excit_L5,0.04741369715,36.33579572,2,OPC/Excit_L5: 36.336,1.693277281,-18530.83724,-18522.72105,5.400689362,8, std logFC = 5.401,,layer +ABCG2,97,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000118777,EndoMural,2.223753949,Micro,0.1416969614,15.69373067,8,EndoMural/Micro: 15.694,2.171291149,-14281.57978,-14273.46359,4.277720977,8, std logFC = 4.278,,layer +AC109466.1,98,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000241956,Excit_L5,2.875408149,Excit_L5/6,1.64483523,1.748143582,11,Excit_L5/Excit_L5/6: 1.748,2.381282963,-4736.240644,-4728.124451,2.098532254,8, std logFC = 2.099,,layer +STK32A,99,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000169302,OPC,2.019957136,Astro,0.1383572451,14.59957616,6,OPC/Astro: 14.6,1.957761947,-17803.04522,-17795.04681,5.213497939,9, std logFC = 5.213,,broad +RFX4,100,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000111783,Astro,2.487167051,OPC,0.6013049407,4.136282413,24,Astro/OPC: 4.136,2.23519419,-13159.12695,-13151.12854,3.026513325,9, std logFC = 3.027,,broad +ABCG2,101,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000118777,EndoMural,2.223753949,Micro,0.1416969614,15.69373067,11,EndoMural/Micro: 15.694,2.168238435,-14358.17224,-14350.17383,4.264840793,9, std logFC = 4.265,,broad +C3,102,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000125730,Micro,2.52206604,Oligo,0.07219728309,34.93297714,8,Micro/Oligo: 34.933,2.479048886,-19530.91812,-19522.91971,6.030900834,9, std logFC = 6.031,,broad +IGF1,103,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000017427,Inhib,1.207954344,Micro,0.4643309408,2.601494403,15,Inhib/Micro: 2.601,1.085057741,-12337.42096,-12329.42255,2.050834687,9, std logFC = 2.051,,layer +STK32A,104,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000169302,OPC,2.019957136,Astro,0.1383572451,14.59957616,5,OPC/Astro: 14.6,1.960969367,-17786.92561,-17778.9272,5.251108072,9, std logFC = 5.251,,layer +IDS,105,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000010404,Excit_L3/4/5,3.71431134,Excit_L4,3.097842565,1.198999388,7,Excit_L3/4/5/Excit_L4: 1.199,1.822760661,-2652.958629,-2644.960219,1.417233814,9, std logFC = 1.417,,layer +RFX4,106,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000111783,Astro,2.487167051,OPC,0.6013049407,4.136282413,19,Astro/OPC: 4.136,2.235231865,-13002.89052,-12994.89211,3.026793957,9, std logFC = 3.027,,layer +CARMN,107,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000249669,EndoMural,1.643565406,Oligo,0.0263563217,62.35943788,1,EndoMural/Oligo: 62.359,1.62914635,-14151.25905,-14143.26064,4.252610704,9, std logFC = 4.253,,layer +LINC01374,108,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000280560,Micro,2.665601597,Excit_L2/3,0.1209461776,22.03956875,14,Micro/Excit_L2/3: 22.04,2.614088563,-19368.9252,-19360.92679,6.044024163,9, std logFC = 6.044,,layer +KIAA1217,109,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000120549,Excit_L6,4.476844625,Inhib,2.447500224,1.829149833,12,Excit_L6/Inhib: 1.829,3.134164009,-3063.328171,-3055.329761,1.940285622,9, std logFC = 1.94,,layer +SYNPR,110,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000163630,Inhib,3.120004834,Excit,0.8724556595,3.576118511,23,Inhib/Excit: 3.576,2.348625671,-12051.46778,-12043.57473,1.998522788,10, std logFC = 1.999,,broad +SMOC1,111,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198732,OPC,2.695256688,Oligo,0.4193902381,6.426608069,14,OPC/Oligo: 6.427,2.5575356,-12775.37056,-12767.47751,4.206881046,10, std logFC = 4.207,,broad +MECOM,112,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000085276,EndoMural,2.250476203,OPC,0.1016495433,22.1395604,6,EndoMural/OPC: 22.14,2.20961047,-14328.39711,-14320.50406,4.259196264,10, std logFC = 4.259,,broad +BLNK,113,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000095585,Micro,2.002882461,Oligo,0.02678543501,74.77505818,3,Micro/Oligo: 74.775,1.982263748,-18454.05934,-18446.16629,5.800409249,10, std logFC = 5.8,,broad +SMOC1,114,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198732,OPC,2.695256688,Oligo,0.4193902381,6.426608069,13,OPC/Oligo: 6.427,2.558973129,-12649.09865,-12641.2056,4.205190442,10, std logFC = 4.205,,layer +CALM2,115,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000143933,Excit_L3/4/5,4.396157921,Excit_L4,3.807952517,1.154467631,14,Excit_L3/4/5/Excit_L4: 1.154,1.848887924,-2643.918919,-2636.025869,1.414693444,10, std logFC = 1.415,,layer +MECOM,116,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000085276,EndoMural,2.250476203,OPC,0.1016495433,22.1395604,4,EndoMural/OPC: 22.14,2.210046204,-13992.7995,-13984.90645,4.222040776,10, std logFC = 4.222,,layer +BLNK,117,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000095585,Micro,2.002882461,Oligo,0.02678543501,74.77505818,3,Micro/Oligo: 74.775,1.982020944,-17972.63512,-17964.74207,5.739397864,10, std logFC = 5.739,,layer +AC073091.4,118,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000287749,Excit_L5,1.215286255,Excit_L5/6,0.6366974551,1.908734275,8,Excit_L5/Excit_L5/6: 1.909,1.02001771,-4584.970314,-4577.077264,2.061760921,10, std logFC = 2.062,,layer +MYT1,119,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000196132,OPC,1.908497271,Inhib,0.3464032729,5.509466625,17,OPC/Inhib: 5.509,1.752463202,-12392.57639,-12384.77865,4.128283609,11, std logFC = 4.128,,broad +PAMR1,120,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000149090,Astro,2.120835277,Excit,0.200048431,10.60160915,3,Astro/Excit: 10.602,1.952587157,-12481.13615,-12473.33841,2.928504013,11, std logFC = 2.929,,broad +SYNE2,121,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000054654,EndoMural,2.567241961,OPC,0.3409544082,7.529575506,22,EndoMural/OPC: 7.53,2.395636832,-13465.04564,-13457.2479,4.094813686,11, std logFC = 4.095,,broad +IKZF1,122,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000185811,Micro,2.062837557,EndoMural,0.1349670194,15.28401209,22,Micro/EndoMural: 15.284,2.044672516,-18057.65993,-18049.86219,5.71549041,11, std logFC = 5.715,,broad +MYT1,123,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000196132,OPC,1.908497271,Inhib,0.3464032729,5.509466625,16,OPC/Inhib: 5.509,1.755374759,-12304.9398,-12297.14206,4.133464035,11, std logFC = 4.133,,layer +LINC01378,124,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000236922,Excit_L3,1.599490081,Excit_L5,1.049665513,1.52380931,8,Excit_L3/Excit_L5: 1.524,1.308563463,-9071.786765,-9063.989026,1.631114364,11, std logFC = 1.631,,layer +COL5A3,125,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000080573,Astro,2.302844157,EndoMural,0.6570010778,3.505084291,22,Astro/EndoMural: 3.505,2.123110144,-12625.76528,-12617.96754,2.971429657,11, std logFC = 2.971,,layer +SYNE2,126,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000054654,EndoMural,2.567241961,OPC,0.3409544082,7.529575506,19,EndoMural/OPC: 7.53,2.39662461,-13427.85074,-13420.053,4.112684037,11, std logFC = 4.113,,layer +IKZF1,127,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000185811,Micro,2.062837557,EndoMural,0.1349670194,15.28401209,19,Micro/EndoMural: 15.284,2.044819961,-17628.70658,-17620.90884,5.664314641,11, std logFC = 5.664,,layer +TRABD2A,128,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000186854,Excit_L5,0.7658593859,Excit_L5/6,0.3389938883,2.25921296,3,Excit_L5/Excit_L5/6: 2.259,0.681590249,-4356.309844,-4348.512105,2.005299079,11, std logFC = 2.005,,layer +SLC12A8,129,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000221955,Excit_L2/3,0.9334811335,Excit_L3,0.6588626127,1.416806957,2,Excit_L2/3/Excit_L3: 1.417,0.586042251,-50.2780032,-42.48026383,1.074640783,11, std logFC = 1.075,,layer +GRIN3A,130,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198785,Inhib,1.730543079,Excit,0.2999986878,5.76850216,12,Inhib/Excit: 5.769,1.438277757,-11545.03817,-11537.32744,1.946698438,12, std logFC = 1.947,,broad +NTN1,131,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000065320,OPC,1.78926937,EndoMural,0.4206738151,4.253341439,21,OPC/EndoMural: 4.253,1.712181691,-11287.19113,-11279.4804,3.898727563,12, std logFC = 3.899,,broad +SYK,132,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000165025,Micro,2.053288275,Inhib,0.09744501314,21.07125043,16,Micro/Inhib: 21.071,2.029754872,-17869.42799,-17861.71726,5.675143288,12, std logFC = 5.675,,broad +GRIN3A,133,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198785,Inhib,1.730543079,Excit_L5,0.7667857832,2.256879452,24,Inhib/Excit_L5: 2.257,1.446197713,-11453.13181,-11445.42109,1.958781122,12, std logFC = 1.959,,layer +CACNG4,134,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000075461,OPC,1.829603104,Inhib,0.3062213481,5.9747732,15,OPC/Inhib: 5.975,1.751404365,-11050.48585,-11042.77512,3.868906114,12, std logFC = 3.869,,layer +LINC02296,135,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000258859,Excit_L3,1.03274461,Excit_L2/3,0.4288175933,2.408354103,1,Excit_L3/Excit_L2/3: 2.408,0.9144674288,-8875.572722,-8867.861994,1.610300614,12, std logFC = 1.61,,layer +PAMR1,136,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000149090,Astro,2.120835277,Excit_L3,0.3288294631,6.449651005,8,Astro/Excit_L3: 6.45,1.956115238,-12358.76127,-12351.05054,2.932072018,12, std logFC = 2.932,,layer +NOTCH3,137,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000074181,EndoMural,1.573248357,Excit_L2/3,0.1117919159,14.07300648,9,EndoMural/Excit_L2/3: 14.073,1.548395314,-12627.72396,-12620.01324,3.956673595,12, std logFC = 3.957,,layer +SYK,138,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000165025,Micro,2.053288275,Inhib,0.09744501314,21.07125043,15,Micro/Inhib: 21.071,2.029918564,-17422.46491,-17414.75418,5.619268791,12, std logFC = 5.619,,layer +AC007368.1,139,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000255595,Excit_L5/6,2.268679446,Excit_L3,1.576194201,1.439340054,16,Excit_L5/6/Excit_L3: 1.439,1.545775475,-2669.146392,-2661.435664,1.547489937,12, std logFC = 1.547,,layer +COL12A1,140,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000111799,Excit_L5,1.372629483,Excit_L5/6,0.5188828551,2.645355246,2,Excit_L5/Excit_L5/6: 2.645,1.07218827,-3537.679177,-3529.968449,1.792978663,12, std logFC = 1.793,,layer +KIT,141,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000157404,Inhib,1.559251717,OPC,0.192720352,8.090747556,8,Inhib/OPC: 8.091,1.246343794,-10240.0014,-10232.37071,1.81090138,13, std logFC = 1.811,,broad +CACNG4,142,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000075461,OPC,1.829603104,Inhib,0.3062213481,5.9747732,16,OPC/Inhib: 5.975,1.740927111,-10839.80173,-10832.17104,3.804545954,13, std logFC = 3.805,,broad +SLC25A18,143,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000182902,Astro,1.878715421,Oligo,0.2333016133,8.052732229,8,Astro/Oligo: 8.053,1.738395843,-11293.23757,-11285.60688,2.754404576,13, std logFC = 2.754,,broad +ITGA1,144,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000213949,EndoMural,1.759660119,Inhib,0.09437121122,18.6461538,9,EndoMural/Inhib: 18.646,1.695886668,-12584.89579,-12577.26511,3.925571863,13, std logFC = 3.926,,broad +NTN1,145,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000065320,OPC,1.78926937,EndoMural,0.4206738151,4.253341439,18,OPC/EndoMural: 4.253,1.710917109,-10986.16239,-10978.53171,3.855189853,13, std logFC = 3.855,,layer +ENC1,146,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000171617,Excit_L3/4/5,3.324038993,Excit_L3,2.711415538,1.225942297,5,Excit_L3/4/5/Excit_L3: 1.226,1.964480545,-2462.002024,-2454.371338,1.362750654,13, std logFC = 1.363,,layer +SLC25A18,147,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000182902,Astro,1.878715421,Oligo,0.2333016133,8.052732229,5,Astro/Oligo: 8.053,1.764813824,-12083.41538,-12075.7847,2.891334822,13, std logFC = 2.891,,layer +CLDN5,148,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000184113,EndoMural,1.744698836,Oligo,0.05984230862,29.15493865,2,EndoMural/Oligo: 29.155,1.702327822,-12486.89432,-12479.26363,3.929057898,13, std logFC = 3.929,,layer +DPP4,149,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000197635,Excit_L6,0.6591071977,Excit_L4,0.1867582532,3.529199841,4,Excit_L6/Excit_L4: 3.529,0.5823384537,-2739.020873,-2731.390187,1.82897994,13, std logFC = 1.829,,layer +SAMD5,150,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000203727,Inhib,1.963588183,Excit,0.371944217,5.279254503,15,Inhib/Excit: 5.279,1.510233132,-10233.80262,-10226.24604,1.810247485,14, std logFC = 1.81,,broad diff --git a/panel_design/7.csv b/panel_design/7.csv new file mode 100644 index 0000000000000000000000000000000000000000..362b56c8b321c617e56c491d6da5a9159e152f98 --- /dev/null +++ b/panel_design/7.csv @@ -0,0 +1,152 @@ +gene_name,annotation,top_50,top_100,top_150 +ADGRV1,DE in astrocyte in reference dataset,1,1,1 +SLC1A3,astrocyte marker literature,1,1,1 +SLC1A2,astrocyte marker literature,1,1,1 +CDH20,"DE in Oligo, OPC, astrocyte in reference dataset",1,1,1 +PTPRZ1,DE in OPC and astrocytesin reference dataset,1,1,1 +ST18,DE in Oligodendrocyte in reference dataset,1,1,1 +MBP,Oligodendrocyte marker / gene linked with methylation,1,1,1 +PTGDS,oligodendrocyte subtype marker in literature,1,1,1 +SST,marker of sstGABAergic cortinal interneuron,1,1,1 +GAD1,GABAergin neuronal maker,1,1,1 +GAD2,GABAergin neuronal maker,1,1,1 +ADARB2,GABAergic neurons marker,1,1,1 +SOX6,"DE in reference dataset: sstGABAnergic, pvalb GABAnergic neurons, OPC, astrocyte DE",1,1,1 +SATB2,glutaminergic neuronal marker + DE in dataset,1,1,1 +HS3ST4,glutaminergic neuronal marker + DE in dataset,1,1,1 +TSHZ2,cortical neuron marker,1,1,1 +RTN1,broad neuronal marker,1,1,1 +NFIB,marker of several GABAergic neurons,1,1,1 +MAP2,mature neuronal marker,1,1,1 +LHFPL3,DE in OPC in reference dataset,1,1,1 +DSCAM,DE in OPC and vip-GAB in referece,1,1,1 +CTNNA3,DE in oligo in reference dataset,1,1,1 +EGFR,"GABAergic cortinal interneuron, literature + DE in reference dataset",1,1,1 +NXPH2,Marker of GABAergic + DE in GABAergic cortinal interneuron in reference,1,1,1 +CUX2,DE expressed in neurons reference dataset,1,1,1 +RXFP1,glutaminergic neuronal marker + DE in dataset,1,1,1 +KCNIP4,interneuron and OPC + DE in reference dataset,1,1,1 +MEF2C,Marker of glutamatergic neurons,1,1,1 +CHL1,Marker of neuroplasticity/neurotropic,1,1,1 +GRIK4,gene involved in synaptic signaling,1,1,1 +GRIN2B,gene involved in synaptic signaling,1,1,1 +PLP1,genes linked to myelination,1,1,1 +SYT1,genes linked to calcium/calmodulin pathways,1,1,1 +ATP2B2,gene linked to metabolic alteratsion,1,1,1 +ATP1B1,gene linked to metabolic alteratsion,1,1,1 +SYNDIG1,microglia marker gene in lit + DE in ref data,1,1,1 +HSP90AA1,microglial subtype marker,1,1,1 +ETV5,astrocyte suptype marker,1,1,1 +STMN2,broad neuronal markers,1,1,1 +KCNJ6,Dopaminergic neuron marker,1,1,1 +UNC13C,GABAergic neuron marker,1,1,1 +ITM2B,gene linked to B-amyloid aggregation,1,1,1 +GRIA1,gene linked to glutamate transport,1,1,1 +GRIA2,gene linked to glutamate transport,1,1,1 +CAMK2A,gene linked to neurotransmitter pathways,1,1,1 +CALM2,genes linked to calcium/calmodulin pathways,1,1,1 +CAMK4,genes linked to calcium/calmodulin pathways,1,1,1 +FYN,genes linked to inflammation/immune response,1,1,1 +CALM1,genes linked to calcium/calmodulin pathways,1,1,1 +ATP1A1,gene linked to metabolic alteratsion,1,1,1 +P2RY12,homeostatic microglial gene,0,1,1 +P2RY12,activated microglial makers,0,1,1 +FGFR3,astrocyte marker literature,0,1,1 +PDGFRA,OPC maker + DE in reference dataset,0,1,1 +OPALIN,oligodendrocyte marker in literature + DE in reference dataset,0,1,1 +MOG,mature oligodendrocyte marker,0,1,1 +VIP,marker of vip GABAergic cortinal interneuron,0,1,1 +PROX1,GABAergic cortinal interneuron marker + DE,0,1,1 +SULF1,subtypes of glutaminergic neuronal also DE in dataset,0,1,1 +GLUL,astrocyte marker literature,0,1,1 +MERTK,astrocyte suptype marker from lit,0,1,1 +SIRT2,cell cycle genes,0,1,1 +RGS5,pericyte marker,0,1,1 +LHX6,"GABAergic cortinal interneuron, lit, DE",0,1,1 +SLC17A7,glutamatergic neuron,0,1,1 +ATP1A2,gene linked to metabolic alteratsion,0,1,1 +BIN1,microglia marker,0,1,1 +NFKB1,inflammatory microglial marker gene,0,1,1 +HIF1A,microglial subtype marker,0,1,1 +LAMP1,expressed in some microglia,0,1,1 +ATP1B2,astrocyte marker literature,0,1,1 +HOPX,oligodendrocyte subtype marker in literature,0,1,1 +NEFL,neuronal marker,0,1,1 +APOE,linked to B-amyloid aggregation,0,1,1 +CST3,linked to B-amyloid aggregation,0,1,1 +SET,gene associated with neuroplasticity/neurotropic,0,1,1 +PCP4,gene associated with neuroplasticity/neurotropic,0,1,1 +PTPRN,gene associated with cell-cell signaling,0,1,1 +PIK3CA,gene associated with cell migration,0,1,1 +CPLX2,gene associated with synaptic signaling,0,1,1 +NDUFA4,gene linked to metabolic alteratsion,0,1,1 +ATP5F1D,gene linked to metabolic alteratsion,0,1,1 +MDH1,gene linked to metabolic alteratsion,0,1,1 +COX4I1,gene linked to metabolic alteratsion,0,1,1 +NCAN,gene associated with biosynthesis,0,1,1 +RPL15,gene associated with biosynthesis,0,1,1 +PSMC6,gene associated with proteosome,0,1,1 +PSMA1,gene associated with proteosome,0,1,1 +MAPT,mature neuronal marker,0,1,1 +ITM2C,linked to B-amyloid aggregation,0,1,1 +APBB1,linked to B-amyloid aggregation,0,1,1 +WASL,gene associated with cell migration,0,1,1 +ARPC3,gene associated with cell migration,0,1,1 +SCN1B,gene associated with synaptic signaling,0,1,1 +PRKCG,gene associated with neurotransmitter pathways,0,1,1 +NDUFV3,gene linked to metabolic alteratsion,0,1,1 +ATP5F1B,gene linked to metabolic alteratsion,0,1,1 +ATP5F1A,gene linked to metabolic alteratsion,0,1,1 +MRPL57,gene associated with biosynthesis,0,1,1 +EEF1A2,gene associated with biosynthesis,0,1,1 +FARSB,gene associated with biosynthesis,0,1,1 +BLNK,microglia DE preivous paper + DE in ref data,0,0,1 +MRC1,"activated microglial makers in literature, DE in reference dataset",0,0,1 +CD14,"microglia marked in literature, DE in reference dataset",0,0,1 +CX3CR1,homeostatic microglial gene,0,0,1 +CD74,microglia marker,0,0,1 +SPI1,microglia marker,0,0,1 +C1QB,microglia marker,0,0,1 +GFAP,"astrocyte marker in literature, DE in reference dataset",0,0,1 +AQP4,"astrocyte marker in literature, DE in reference dataset",0,0,1 +AGT,astrocyte marker literature,0,0,1 +GJB6,astrocyte marker literature,0,0,1 +SOX10,oligodendrocyte marker in literature,0,0,1 +OLIG1,oligodendrocyte marker in literature,0,0,1 +OLIG2,oligodendrocyte marker in literature,0,0,1 +MAG,Myelinating Oligodendrocyte Markers,0,0,1 +KLK6,oligodendrocyte subtype marker in literature,0,0,1 +ASPA,mature oligodendrocyte marker,0,0,1 +ITM2A,endothelial marker lit,0,0,1 +PCNA,cell cycle genes,0,0,1 +MCM6,cell cycle genes,0,0,1 +ACTA2,pericyte marker,0,0,1 +PVALB,marker of pvalb GABAergic cortinal interneuron,0,0,1 +LAMP5,marker of lamp5 GABAergic cortical interneuron,0,0,1 +CALB2,"vip GABAergic cortinal interneuron, literature + DE in reference dataset",0,0,1 +SNCG,projecting glutaminergic cortical,0,0,1 +SYT6,DE in microglia in reference dataset,0,0,1 +SOX9,astrocyte marker literature,0,0,1 +SLC7A10,neural stem cells marker /astrocyte suptype marker from lit,0,0,1 +ID3,astrocyte suptype marker from lit,0,0,1 +WFS1,astrocyte suptype marker from lit,0,0,1 +FAM107A,astrocyte suptype marker from lit,0,0,1 +ZNF488,mature oligodendrocyte marker,0,0,1 +CHRNA2,"vip GABAergic cortinal interneuron, literature + DE in reference dataset",0,0,1 +PTPRC,immune marker,0,0,1 +CEBPB,senescent microglia marker,0,0,1 +NLRP3,,0,0,1 +CHODL,"oligodendrocyte marker in literature, DE in reference dataset",0,0,1 +ANXA5,oligodendrocyte subtype marker in literature,0,0,1 +OTOF,"sstGABAergic cortinal interneuron, lit, DE",0,0,1 +MAL,genes linked to myelination,0,0,1 +PRKX,genes linked to inflammation/immune response,0,0,1 +FRZB,astrocyte suptype marker from lit,0,0,1 +S100B,astrocyte marker literature,0,0,1 +NPY,Cell-cell signaling,0,0,1 +PCDH8,Cell-cell signaling,0,0,1 +TSPAN2,genes linked to myelination,0,0,1 +COX8A,gene linked to metabolic alteratsion,0,0,1 +RPN1,Proteosome,0,0,1 +RELB,inflammatory microglial marker gene,0,0,1 +NDUFS7,gene linked to metabolic alteratsion,0,0,1 diff --git a/panel_design/8.csv b/panel_design/8.csv new file mode 100644 index 0000000000000000000000000000000000000000..ee5263703363eb7bf4802b41a1a0959673639477 --- /dev/null +++ b/panel_design/8.csv @@ -0,0 +1,151 @@ +Unnamed: 0,soma_joinid,feature_id,feature_name,feature_length,nnz,n_measured_obs,highly_variable,means,dispersions,dispersions_norm,Annotation & Reasoning,Ranking +0,5241,ENSG00000122585,NPY,893,1487637,69587951,True,0.2784628391503804,5.472432619245862,8.076506,Based on the hvgs with best normalization method,top50 +1,3360,ENSG00000107099,DOCK8,20056,11548217,74513630,True,0.52630464178451,4.0060010387337766,6.769335,Based on the hvgs with best normalization method,top50 +2,1377,ENSG00000077420,APBB1IP,3843,11013041,74541465,True,0.46146377718864,3.8968569972808864,6.524311,Based on the hvgs with best normalization method,top50 +3,22073,ENSG00000234377,OBI1-AS1,10180,2521757,61741141,True,0.9734908521337996,3.958334930562165,6.3384104,Based on the hvgs with best normalization method,top50 +4,32293,ENSG00000277632,CCL3,1494,3866143,61139156,True,0.0093135161911686,4.393203181327225,6.1793547,Based on the hvgs with best normalization method,top50 +5,12173,ENSG00000168314,MOBP,9237,4846625,72513409,True,0.9614663545220036,3.858070407540572,6.1201987,Based on the hvgs with best normalization method,top50 +6,4810,ENSG00000118785,SPP1,7250,8595089,73920484,True,0.5587869816521938,3.706557523761285,6.0970974,Based on the hvgs with best normalization method,top50 +7,3383,ENSG00000107317,PTGDS,2712,10225627,74204733,True,1.2863731638684015,3.751850425826654,5.836781,Based on the hvgs with best normalization method,top50 +8,13218,ENSG00000172987,HPSE2,4421,3904787,73047912,True,1.2129782158058935,3.7012206035381774,5.724114,Based on the hvgs with best normalization method,top50 +9,381,ENSG00000018625,ATP1A2,6298,4848403,73460061,True,0.7347201184171539,3.663378427183657,5.696479,Based on the hvgs with best normalization method,top50 +10,548,ENSG00000038427,VCAN,14678,12407214,74552031,True,0.7631149795408386,3.600036576582366,5.5586243,Based on the hvgs with best normalization method,top50 +11,17512,ENSG00000204389,HSPA1A,2404,20330619,64819739,True,0.3483590391423253,3.464540264521311,5.553779,Based on the hvgs with best normalization method,top50 +12,8855,ENSG00000147246,HTR2C,4842,3271887,58827748,True,0.7858794209017294,3.5926348724054007,5.5425153,Based on the hvgs with best normalization method,top50 +13,25247,ENSG00000251372,LINC00499,20131,1680795,57253942,True,0.5150609102577905,3.4490050961280407,5.5189033,Based on the hvgs with best normalization method,top50 +14,11877,ENSG00000167123,CERCAM,6058,6253645,74320849,True,0.7828653201335859,3.565729125607589,5.4839587,Based on the hvgs with best normalization method,top50 +15,12400,ENSG00000169313,P2RY12,2337,2640423,71547277,True,0.4689977371721238,3.4120002061266947,5.435829,Based on the hvgs with best normalization method,top50 +16,6331,ENSG00000131095,GFAP,11229,2603965,73350833,True,0.2876280341852898,3.936925670728887,5.377276,Based on the hvgs with best normalization method,top50 +17,10161,ENSG00000158865,SLC5A11,3415,3364055,69923585,True,0.574600980890718,3.376484619946382,5.356098,Based on the hvgs with best normalization method,top50 +18,9219,ENSG00000150656,CNDP1,7117,3107345,74297237,True,0.5260503757110235,3.324706229330675,5.239858,Based on the hvgs with best normalization method,top50 +19,20711,ENSG00000229807,XIST,25266,21056286,74315539,True,1.1813301289979623,3.466603367504034,5.2020183,Based on the hvgs with best normalization method,top50 +20,11123,ENSG00000164124,TMEM144,9248,8045941,74437632,True,0.9652175529364386,3.4310867041448723,5.1909285,Based on the hvgs with best normalization method,top50 +21,31901,ENSG00000275302,CCL4,1795,6143173,62760344,True,0.0025910273861521,3.8228119051090586,5.176678,Based on the hvgs with best normalization method,top50 +22,15192,ENSG00000184113,CLDN5,3429,2524056,74446360,True,0.0437600914665603,3.819325395509967,5.1705494,Based on the hvgs with best normalization method,top50 +23,25766,ENSG00000253807,LINC01170,3850,3703423,68171161,True,0.5707032423248326,3.2852530352095077,5.151287,Based on the hvgs with best normalization method,top50 +24,33730,ENSG00000180229,HERC2P3_ENSG00000180229,4088,1827785,27752448,True,0.2803260141440332,3.767946887391629,5.080232,Based on the hvgs with best normalization method,top50 +25,1491,ENSG00000080573,COL5A3,6783,5447597,74449798,True,0.7555858023716054,3.378410803350078,5.076287,Based on the hvgs with best normalization method,top50 +26,9666,ENSG00000154493,C10orf90,5659,4440265,67152709,True,0.875581664155515,3.374201105219235,5.0671253,Based on the hvgs with best normalization method,top50 +27,8761,ENSG00000146469,VIP,1585,1138785,68691606,True,0.7213746131758096,3.3694517010030616,5.056789,Based on the hvgs with best normalization method,top50 +28,8882,ENSG00000147459,DOCK5,15989,10823963,74290291,True,0.9082107891458808,3.368977884127573,5.0557575,Based on the hvgs with best normalization method,top50 +29,27091,ENSG00000257585,LINC00609,583,4992807,50838830,True,0.7328977593171419,3.348563758499239,5.011329,Based on the hvgs with best normalization method,top50 +30,7309,ENSG00000136960,ENPP2,6729,6966932,74560519,True,0.7944294216609206,3.333032659221951,4.9775276,Based on the hvgs with best normalization method,top50 +31,8449,ENSG00000144229,THSD7B,6708,6341082,70419221,True,0.9194696182982336,3.310702524025904,4.9289293,Based on the hvgs with best normalization method,top50 +32,15168,ENSG00000183960,KCNH8,6088,6157298,74266159,True,0.883848466775706,3.3051588757014865,4.9168644,Based on the hvgs with best normalization method,top50 +33,2510,ENSG00000101439,CST3,3615,27512197,74668992,True,0.6748217603618987,3.1795989384367074,4.9140983,Based on the hvgs with best normalization method,top50 +34,11191,ENSG00000164330,EBF1,6316,6862033,74452348,True,0.0808396016860488,3.669852728684886,4.907795,Based on the hvgs with best normalization method,top50 +35,15531,ENSG00000185650,ZFP36L1,6466,26391142,74394567,True,0.3487557774474227,3.173621365905224,4.900679,Based on the hvgs with best normalization method,top50 +36,16783,ENSG00000197520,FAM177B,2805,1011468,74266705,True,0.1702217044119632,3.648502115335857,4.870263,Based on the hvgs with best normalization method,top50 +37,9936,ENSG00000157005,SST,607,2166462,63111103,True,0.794279151054743,3.2809449870199447,4.8641663,Based on the hvgs with best normalization method,top50 +38,17608,ENSG00000204655,MOG,3175,2662310,63168628,True,0.5889720942517631,3.155045946295618,4.8589783,Based on the hvgs with best normalization method,top50 +39,4659,ENSG00000117318,ID3,1496,10773972,72735199,True,0.11467277785575,3.636231982980021,4.848694,Based on the hvgs with best normalization method,top50 +40,13126,ENSG00000172508,CARNS1,5670,2858746,74329638,True,0.4607257690794789,3.13743267495587,4.819437,Based on the hvgs with best normalization method,top50 +41,7026,ENSG00000135540,NHSL1,8741,8516367,74564094,True,0.6323054715980764,3.1336152777375865,4.8108673,Based on the hvgs with best normalization method,top50 +42,3748,ENSG00000110436,SLC1A2,22800,14723654,74245583,True,2.2372810686961864,5.118459519668878,4.8069806,Based on the hvgs with best normalization method,top50 +43,30347,ENSG00000268751,SCGB1B2P,754,513073,71291662,True,0.0023219452044399,3.5963341012498677,4.7785583,Based on the hvgs with best normalization method,top50 +44,15219,ENSG00000184221,OLIG1,2273,2770870,73073562,True,0.3798056667882042,3.103276777107508,4.7427588,Based on the hvgs with best normalization method,top50 +45,13504,ENSG00000174607,UGT8,4385,4502125,74250259,True,0.5682002703328997,3.0803347057375188,4.6912546,Based on the hvgs with best normalization method,top50 +46,16760,ENSG00000197430,OPALIN,3874,1835848,56197923,True,0.4221487818214699,3.0707599776788013,4.6697598,Based on the hvgs with best normalization method,top50 +47,4266,ENSG00000114541,FRMD4B,11201,16560570,74505631,True,1.222521920193563,3.2186813547572046,4.6503153,Based on the hvgs with best normalization method,top50 +48,327,ENSG00000013297,CLDN11,4321,3987554,72517586,True,0.5142272418796419,3.052022016031001,4.6276937,Based on the hvgs with best normalization method,top50 +49,3147,ENSG00000105695,MAG,2960,2791162,73743438,True,0.4186199035688159,3.0412205225704634,4.603445,Based on the hvgs with best normalization method,top50 +50,5351,ENSG00000123560,PLP1,6088,6982636,70276834,True,1.704900313728858,4.6528393089055085,4.5909967,Based on the hvgs with best normalization method,top100 +51,1618,ENSG00000084453,SLCO1A2,11524,2884484,71671661,True,0.4585991876673033,3.028782719125683,4.5755224,Based on the hvgs with best normalization method,top100 +52,6161,ENSG00000130203,APOE,2154,12430586,74637406,True,0.4222819610948677,3.0165719918308875,4.54811,Based on the hvgs with best normalization method,top100 +53,4773,ENSG00000118432,CNR1,6345,8991633,74294563,True,1.265042676927463,3.169683594582363,4.5412803,Based on the hvgs with best normalization method,top100 +54,895,ENSG00000064787,BCAS1,10533,4663794,74474849,True,0.7391818799326076,3.119855545502296,4.513578,Based on the hvgs with best normalization method,top100 +55,8003,ENSG00000141338,ABCA8,11246,5532225,72981313,True,0.4489079817334538,2.998058009662332,4.506547,Based on the hvgs with best normalization method,top100 +56,16969,ENSG00000198121,LPAR1,4137,6467341,74560584,True,0.7618253824496256,3.1147901955503943,4.502554,Based on the hvgs with best normalization method,top100 +57,12174,ENSG00000168329,CX3CR1,3656,2537803,74287878,True,0.204655545268738,3.438573298048609,4.5012345,Based on the hvgs with best normalization method,top100 +58,1237,ENSG00000073849,ST6GAL1,11142,15534400,74572847,True,1.1982050631253789,3.135386706349258,4.464959,Based on the hvgs with best normalization method,top100 +59,8888,ENSG00000147488,ST18,14438,6471842,73827740,True,1.577247400151292,4.546193079758141,4.440893,Based on the hvgs with best normalization method,top100 +60,1919,ENSG00000091513,TF,26038,9640434,74021614,True,1.3027290464455048,3.118480425787,4.427337,Based on the hvgs with best normalization method,top100 +61,10082,ENSG00000158270,COLEC12,7343,5421572,74526534,True,0.1283533079912794,3.3910721924048253,4.4177337,Based on the hvgs with best normalization method,top100 +62,16276,ENSG00000189056,RELN,35421,5989024,72845284,True,1.482391183112545,4.527350413214273,4.4143724,Based on the hvgs with best normalization method,top100 +63,57505,ENSG00000284160,MIR7706,67,320,4558058,True,0.000254312790952,3.3613201936542554,4.365433,Based on the hvgs with best normalization method,top100 +64,6893,ENSG00000134853,PDGFRA,9547,4133848,74205232,True,0.4095718553912153,2.9347512477217723,4.364426,Based on the hvgs with best normalization method,top100 +65,10090,ENSG00000158352,SHROOM4,15184,4356923,68572200,True,0.5218087821649494,2.934423219412552,4.36369,Based on the hvgs with best normalization method,top100 +66,942,ENSG00000065809,FAM107B,7019,17633084,74572847,True,0.5555093594169203,2.9341386699319374,4.363051,Based on the hvgs with best normalization method,top100 +67,5560,ENSG00000125148,MT2A,914,24572055,74524461,True,0.2522153061080633,3.3475597892810303,4.341244,Based on the hvgs with best normalization method,top100 +68,1679,ENSG00000086205,FOLH1,5335,2928011,71970665,True,0.3628899572923751,2.9220987639981115,4.336022,Based on the hvgs with best normalization method,top100 +69,1855,ENSG00000090104,RGS1,4074,6643913,74375774,True,0.0293400730240709,3.3439888948949217,4.334967,Based on the hvgs with best normalization method,top100 +70,7396,ENSG00000137491,SLCO2B1,10277,4449905,74236127,True,0.2450073189899416,3.340218262666361,4.3283386,Based on the hvgs with best normalization method,top100 +71,1025,ENSG00000068078,FGFR3,4848,2468727,73293979,True,0.3871606803865989,2.917813419730002,4.326401,Based on the hvgs with best normalization method,top100 +72,4646,ENSG00000117215,PLA2G2D,2681,80697,73080509,True,0.0005875360712289,3.335563492475841,4.320156,Based on the hvgs with best normalization method,top100 +73,464,ENSG00000028116,VRK2,3531,9129288,74502763,True,0.4571411935658678,2.911487996657224,4.312201,Based on the hvgs with best normalization method,top100 +74,9446,ENSG00000152518,ZFP36L2,3693,27332772,74216795,True,0.31513478008652,3.32672126012961,4.3046126,Based on the hvgs with best normalization method,top100 +75,16071,ENSG00000188153,COL4A5,11871,6328106,73114575,True,0.5128218347917031,2.902175786769859,4.2912955,Based on the hvgs with best normalization method,top100 +76,25021,ENSG00000250722,SELENOP,5502,12343990,66178131,True,0.3831500387134665,2.899454711442124,4.285187,Based on the hvgs with best normalization method,top100 +77,27678,ENSG00000259070,LINC00639,9453,2985688,69780519,True,0.4345296954736012,2.896433940788761,4.2784057,Based on the hvgs with best normalization method,top100 +78,7072,ENSG00000135821,GLUL,12638,24312926,74400727,True,0.6637534522594922,2.8934367421432725,4.271677,Based on the hvgs with best normalization method,top100 +79,7168,ENSG00000136250,AOAH,3518,9127086,74627767,True,0.4765791257977578,2.890629823868756,4.2653756,Based on the hvgs with best normalization method,top100 +80,8714,ENSG00000146122,DAAM2,12955,5616378,74113794,True,0.6971061682850775,2.9942330891377824,4.240178,Based on the hvgs with best normalization method,top100 +81,5639,ENSG00000125730,C3_ENSG00000125730,11577,5625071,74572198,True,0.1863441181735022,3.2771579168293976,4.2174864,Based on the hvgs with best normalization method,top100 +82,9748,ENSG00000155307,SAMSN1,5185,9277875,74484680,True,0.1796204277436855,3.27512934432833,4.2139206,Based on the hvgs with best normalization method,top100 +83,2741,ENSG00000103089,FA2H,3279,3529192,74201872,True,0.4488143386102362,2.855949955123439,4.1875205,Based on the hvgs with best normalization method,top100 +84,13370,ENSG00000173786,CNP,7413,10634612,68068310,True,0.5775932053867758,2.850697087469193,4.1757283,Based on the hvgs with best normalization method,top100 +85,13735,ENSG00000175899,A2M,6318,9578251,74374953,True,0.3597823122995064,2.8493397666909885,4.1726813,Based on the hvgs with best normalization method,top100 +86,4017,ENSG00000112319,EYA4,14674,4511586,72476380,True,0.6717625269274267,2.847782217464863,4.1691847,Based on the hvgs with best normalization method,top100 +87,1444,ENSG00000079215,SLC1A3,21227,9582156,74406585,True,1.4390292471685913,4.344737590726089,4.157347,Based on the hvgs with best normalization method,top100 +88,609,ENSG00000046889,PREX2,12132,8642053,74004383,True,1.0206908773132053,2.9471314964557203,4.137668,Based on the hvgs with best normalization method,top100 +89,14858,ENSG00000182578,CSF1R,5151,4291984,74457424,True,0.2114800007063883,3.229929765088273,4.134465,Based on the hvgs with best normalization method,top100 +90,22824,ENSG00000236790,LINC00299,23624,6051694,71833857,True,1.207794284898008,2.98642125462354,4.133465,Based on the hvgs with best normalization method,top100 +91,11540,ENSG00000165795,NDRG2,7550,9251140,74333224,True,0.484050325408196,2.826831765286455,4.122152,Based on the hvgs with best normalization method,top100 +92,9070,ENSG00000149090,PAMR1,3861,4123680,68354158,True,0.6077380143589539,2.8248041935470094,4.1176,Based on the hvgs with best normalization method,top100 +93,11151,ENSG00000164199,ADGRV1,33822,11676625,65661938,True,1.5122817302150076,4.310319621834209,4.1089044,Based on the hvgs with best normalization method,top100 +94,2679,ENSG00000102755,FLT1,12575,4248956,74491361,True,0.176550708931357,3.2082475484185897,4.0963507,Based on the hvgs with best normalization method,top100 +95,12305,ENSG00000168918,INPP5D,8681,8098619,73331347,True,0.316509087020153,3.2078499774165667,4.0956516,Based on the hvgs with best normalization method,top100 +96,11148,ENSG00000164188,RANBP3L,4884,2795980,72610757,True,0.4411228413386047,2.8144874319792192,4.094439,Based on the hvgs with best normalization method,top100 +97,8772,ENSG00000146592,CREB5,11681,13818231,74523823,True,1.0205085582340396,2.922207803928156,4.083425,Based on the hvgs with best normalization method,top100 +98,4089,ENSG00000112902,SEMA5A,12308,9671805,74530046,True,0.9880056438061656,2.920265662984906,4.0791984,Based on the hvgs with best normalization method,top100 +99,5548,ENSG00000124920,MYRF,10773,3322912,74238984,True,0.3687125871338419,2.7983769104151883,4.058272,Based on the hvgs with best normalization method,top100 +100,5317,ENSG00000123243,ITIH5,14628,2785886,73548537,True,0.0666306529037455,3.182538750378953,4.051158,Based on the hvgs with best normalization method,top150 +101,729,ENSG00000054690,PLEKHH1,10828,9843268,74252079,True,0.9110920306569854,2.897885880897208,4.030492,Based on the hvgs with best normalization method,top150 +102,7535,ENSG00000138135,CH25H,1689,1875442,74300862,True,0.0549956938231348,3.170092250184101,4.0292783,Based on the hvgs with best normalization method,top150 +103,794,ENSG00000059377,TBXAS1,6177,6783362,74505631,True,0.2733194386127924,3.1696655084767515,4.028528,Based on the hvgs with best normalization method,top150 +104,34011,ENSG00000197085,NPSR1-AS1,7106,1945065,43302291,True,0.4017795194522703,2.7841173350661497,4.0262594,Based on the hvgs with best normalization method,top150 +105,9343,ENSG00000151702,FLI1,8026,7693942,74464122,True,0.166115425149204,3.1663141175357663,4.022637,Based on the hvgs with best normalization method,top150 +106,5470,ENSG00000124440,HIF3A,8375,6417477,73415130,True,0.5081225721678853,2.7821104305173714,4.0217543,Based on the hvgs with best normalization method,top150 +107,589,ENSG00000042980,ADAM28,9381,5065136,74357795,True,0.3184194472619878,3.1641660906533566,4.018861,Based on the hvgs with best normalization method,top150 +108,17921,ENSG00000206190,ATP10A,20675,3714316,74517624,True,0.2116144567057585,3.1628948087884106,4.016626,Based on the hvgs with best normalization method,top150 +109,17138,ENSG00000198732,SMOC1,4369,3971650,74485207,True,0.4972434499127843,2.778587589452889,4.0138454,Based on the hvgs with best normalization method,top150 +110,1311,ENSG00000075651,PLD1,9954,9567983,74313755,True,0.6738888928058435,2.76621400939481,3.9860675,Based on the hvgs with best normalization method,top150 +111,9230,ENSG00000150760,DOCK1,8142,11239406,74313755,True,0.8338444532777873,2.877090504683105,3.9852338,Based on the hvgs with best normalization method,top150 +112,16397,ENSG00000196187,TMEM63A,10350,7511569,74335350,True,0.4642410623960073,2.765740941011098,3.9850054,Based on the hvgs with best normalization method,top150 +113,11581,ENSG00000165959,CLMN,15703,12091527,74394567,True,0.7772048300645735,2.873009315245747,3.9763517,Based on the hvgs with best normalization method,top150 +114,15570,ENSG00000185811,IKZF1,10921,10300033,74508828,True,0.1295384347102255,3.139404897617985,3.9753337,Based on the hvgs with best normalization method,top150 +115,3679,ENSG00000109846,CRYAB,4388,11801588,67438197,True,0.454897978900646,2.759070264912562,3.97003,Based on the hvgs with best normalization method,top150 +116,14343,ENSG00000179399,GPC5,3529,11587307,71903796,True,2.03758420374263,4.469553310701985,3.9649782,Based on the hvgs with best normalization method,top150 +117,1546,ENSG00000082074,FYB1,8823,13730361,66094247,True,0.2327257756583629,3.1290969404769595,3.9572136,Based on the hvgs with best normalization method,top150 +118,2712,ENSG00000102934,PLLP,8705,4742334,74513630,True,0.3987087188327542,2.753008681890738,3.956422,Based on the hvgs with best normalization method,top150 +119,10201,ENSG00000159216,RUNX1,15574,14786881,74572847,True,0.2722180689884664,3.121893327708413,3.9445505,Based on the hvgs with best normalization method,top150 +120,11362,ENSG00000165025,SYK,5210,6875168,74511327,True,0.1801222093127282,3.1213129992938726,3.9435306,Based on the hvgs with best normalization method,top150 +121,7781,ENSG00000139679,LPAR6,4350,8865185,74360570,True,0.2398865573657181,3.1200183053913118,3.9412546,Based on the hvgs with best normalization method,top150 +122,16278,ENSG00000189058,APOD,2022,8069876,74310190,True,0.4159060114324395,2.744119699986937,3.9364667,Based on the hvgs with best normalization method,top150 +123,16918,ENSG00000197971,MBP,18730,24604003,74572847,True,1.985262335530433,4.443196799991369,3.9286137,Based on the hvgs with best normalization method,top150 +124,15877,ENSG00000187147,RNF220,9678,13855328,74343349,True,1.5196410359016337,4.170857519432524,3.912613,Based on the hvgs with best normalization method,top150 +125,6628,ENSG00000133048,CHI3L1,3363,2496705,73974467,True,0.0521256280027434,3.101142113196867,3.9080725,Based on the hvgs with best normalization method,top150 +126,9713,ENSG00000154930,ACSS1,8691,6053424,74335350,True,0.4406244976018324,2.724063972952348,3.8914425,Based on the hvgs with best normalization method,top150 +127,14925,ENSG00000182902,SLC25A18,4731,2516176,73651515,True,0.3560778227762268,2.7206031190140614,3.883673,Based on the hvgs with best normalization method,top150 +128,10595,ENSG00000162407,PLPP3,5272,9809927,65683896,True,0.523120469869442,2.7091920625504646,3.8580556,Based on the hvgs with best normalization method,top150 +129,1822,ENSG00000089250,NOS1,13113,4080132,70511297,True,0.4182160889406474,2.7077747622061925,3.854874,Based on the hvgs with best normalization method,top150 +130,5839,ENSG00000127249,ATP13A4,8988,5316409,74318981,True,0.6167409419697688,2.707156076035465,3.8534849,Based on the hvgs with best normalization method,top150 +131,1485,ENSG00000080493,SLC4A4,9331,11765062,74511358,True,1.23618968294693,2.8581558735473145,3.8480349,Based on the hvgs with best normalization method,top150 +132,10739,ENSG00000162944,RFTN2,5776,5477177,74417288,True,0.5797464673418502,2.7036338111424296,3.8455777,Based on the hvgs with best normalization method,top150 +133,14113,ENSG00000178031,ADAMTSL1,13446,6650844,69926125,True,1.286800891696848,2.854915901483216,3.8408248,Based on the hvgs with best normalization method,top150 +134,575,ENSG00000041982,TNC,9589,3467211,73740483,True,0.1207955697293166,3.062139409474133,3.8395107,Based on the hvgs with best normalization method,top150 +135,3545,ENSG00000108691,CCL2,1935,5627111,74296150,True,0.0069795315416886,3.061338989879452,3.8381035,Based on the hvgs with best normalization method,top150 +136,11805,ENSG00000166863,TAC3,1571,1114461,66776690,True,0.3986994983212539,2.6984344035745984,3.8339052,Based on the hvgs with best normalization method,top150 +137,1653,ENSG00000085563,ABCB1,6422,7351887,74484170,True,0.564911107949878,2.6978243717697468,3.8325357,Based on the hvgs with best normalization method,top150 +138,7613,ENSG00000138639,ARHGAP24,7870,12895378,74505631,True,1.4462750932491093,4.112176930169104,3.8300207,Based on the hvgs with best normalization method,top150 +139,20037,ENSG00000227502,MROCKI,3292,539947,69200260,True,0.07961006658828,3.0566153649362704,3.8298001,Based on the hvgs with best normalization method,top150 +140,10038,ENSG00000157890,MEGF11,9837,6040369,72280183,True,0.6653825539156258,2.69540640219595,3.8271074,Based on the hvgs with best normalization method,top150 +141,15913,ENSG00000187416,LHFPL3,3376,10441640,62934146,True,1.8072325137962,4.367171377922477,3.82372,Based on the hvgs with best normalization method,top150 +142,12037,ENSG00000167772,ANGPTL4,2475,3850432,74482740,True,0.1124149674381549,3.051677377334163,3.8211198,Based on the hvgs with best normalization method,top150 +143,9530,ENSG00000153208,MERTK,4133,6308017,74319885,True,0.5181886245237786,2.6861702319452694,3.8063726,Based on the hvgs with best normalization method,top150 +144,1522,ENSG00000081237,PTPRC,15436,18963917,72251824,True,0.1808028507072165,3.0428773562505222,3.8056505,Based on the hvgs with best normalization method,top150 +145,29570,ENSG00000265972,TXNIP,3604,27021024,64057359,True,0.1382867651072039,3.0428323214871136,3.8055713,Based on the hvgs with best normalization method,top150 +146,5713,ENSG00000125968,ID1,1233,8757100,74400727,True,0.0739092016295563,3.031862939152805,3.7862885,Based on the hvgs with best normalization method,top150 +147,17000,ENSG00000198223,CSF2RA_ENSG00000198223,4093,3547369,65696602,True,0.2200005198074259,3.0307859764003435,3.7843952,Based on the hvgs with best normalization method,top150 +148,3361,ENSG00000107104,KANK1,25055,12851850,74564848,True,0.9146634826097708,2.7790279722435693,3.7718143,Based on the hvgs with best normalization method,top150 +149,296,ENSG00000011426,ANLN,5997,4564730,74368053,True,0.3836010395319908,2.6702301478833377,3.770588,Based on the hvgs with best normalization method,top150 diff --git a/panel_design/9.csv b/panel_design/9.csv new file mode 100644 index 0000000000000000000000000000000000000000..357744202c96cbc2c61ed0f02021303b5dac5da7 --- /dev/null +++ b/panel_design/9.csv @@ -0,0 +1,157 @@ +Gene symbol,Ranking,Annotation & reasoning,Additional note,Paper links +,,"First 50 - regional/structural, cell type and subclass/subtype annotation, neuronal activation","In Schizophrenia, downregulation of neuronal activity in the DLPFC has been reported (Smucny et al., 2022 - https://www.nature.com/articles/s41386-021-01089-0). What neuronal cell types activity is affected and what non-neuronal and other cell types are proximal to the affected neuronal cell types?", +SNAP25,1.0,Regional and laminal marker : Gray matter ,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed +MBP,2.0,Regional and laminal marker : White matter ,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed +PCP4,3.0,Regional and laminal marker : L5 ,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed +RELN,4.0,Regional and laminal marker : L1 / Gabaergic neuron subclass: LAMP5/RELN/LHX7,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed +NR4A2,5.0,Regional and laminal marker : L6 ,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed +HTRA1,6.0,Regional and laminal marker : L1 sublayer,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed +SPARC,7.0,Regional and laminal marker : L1 sublayer,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed +CLDN5,8.0,Brain vasculature/endothelial cell marker,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed +AQP4,9.0,Regional and laminal marker : L1 /Astrocyte marker,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed +NeuN,10.0,Neuronal marker,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials +INA,11.0,Neuronal marker,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials +SLC17A6,12.0,Glutamergic neuron marker,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials +SLC17A7,13.0,Glutamergic neuron marker,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials +SLC32A1,14.0,Gabaergic neuron marker ,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials +PTRPC,15.0,Immune cell marker,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +ACTA2,16.0,Smooth muscle cell,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +CEMIP,17.0,VCMC,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +PCDH8,18.0,Glutamergic neuron subclass: L3-3 IT ,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +OPRK1,19.0,Glutamergic neuron subclass: L6-IT 1/2 Glut,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +RORB,20.0,Glutamergic neuron subclass: L3-5IT 1/2/3 Glut,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +FEZF2,21.0,Glutamergic neuron subclass: L5ET,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +HTR2C,22.0,Glutamergic neuron subclass: L5-6 NP,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +SYT6,23.0,Glutamergic neuron subclass: L6 CT,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +CTGF,24.0,Glutamergic neuron subclass: L6 B,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +LAMP5,25.0,Gabaergic neuron subclass: LAMP5/RELN/LHX6,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +LHX6,26.0,Gabaergic neuron subclass: LAMP5/RELN/LHX8,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +VIP,27.0,Gabaergic neuron subclass VIP,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +KCNG1,28.0,Gabaergic neuron subclass VIP KCNG1,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +SST,29.0,Gabaergic neuron subclass SST,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +HGF,30.0,Gabaergic neuron subclass SST HGF,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +PVALB,31.0,Gabaergic neuron subclass SST PVALB,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +CHC,32.0,Gabaergic neuron subclass SST PVALB CHC,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +FABP7,33.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +AQP1,34.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +SLC1A2,35.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +GFAP,36.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +OSMR,37.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +PDGFRA,38.0,"Non neuronal subclass, OPC and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +PCDH15,39.0,"Non neuronal subclass, OPC and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +MOG,40.0,"Non neuronal subclass, Oligodendrocytes and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +CDH7,41.0,"Non neuronal subclass, Oligodendrocytes and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +OPALIN,42.0,"Non neuronal subclass, Oligodendrocytes and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +GSN,43.0,"Non neuronal subclass, Oligodendrocytes and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +CCL3,,"Non neuronal subclass, microglia and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex", +P2RY12,44.0,"Non neuronal subclass, microglia and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +IGKC,45.0,"Immune cell, B cell ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +CD247,46.0,"Immune cell, T cell ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +COLEC12,47.0,"Immune cell, Macrophage","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials +GLDN,,"Non neuronal subclass, microglia and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex", +FOS,48.0,Neuronal activity gene - cFos,"Aparicio et al., 2022 - Current Opinion on the Use of c-Fos in Neuroscience",https://www.mdpi.com/2673-4087/3/4/50 +CALM1,49.0,Neuronal activity gene - Calmodulin 1,"Jensen et al., 2024 - Neurological consequences of human calmodulin mutations +",https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10749624/ +APBB7IP,50.0,"Non neuronal subclass, microglia and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex", +,,Next 50-100 - Synaptic markers and Schizophrenia risk genes in GRNs and Cell-Cell communication ,synaptic markers can aid in identifying cell-cell communications and disease relevant L-R pairs can aid in identifying downstream signalling mechanisms , +NRXN3,51.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +SYN1,52.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +SYN2,53.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +SYN3,54.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +SYP,55.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +SYT1,56.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +STX1A,57.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +VAMP2,58.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +VGAT,59.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +VGLUT1,60.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +VGLUT2,61.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +VGLUT3,62.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +GAP43,63.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +VMAT2,64.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +NRG1,65.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +DLG4,66.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +DLG3,67.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +SHANK1,68.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +SHANK3,69.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +HOMER1,70.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +HOMER2,71.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +HOMER3,72.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +GPHN,73.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers, +ICAM1,74.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5199 +AKT1,75.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5200 +MECP2,76.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5201 +PTK2B,77.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5202 +EPHA2,78.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5203 +RARG,79.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5204 +PML,80.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5205 +EPB41,81.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5206 +DMD,82.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5207 +FOXO1,83.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5208 +TEK,84.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5209 +CDH5,85.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5210 +COL3A1,86.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5211 +HIST1HE,87.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5212 +PRKDC,88.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5213 +HMGB1,89.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5214 +HMGB2,90.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5215 +PDGFB,91.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5216 +CRLF1,92.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5217 +NAMPT,93.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5218 +ANGPT1,94.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5219 +CXCL12,95.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5220 +ANGPT2,96.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5221 +PIK3CB,97.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5222 +SEMA5A,98.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5223 +,,"Key transcription factors (TFs) from cell type specific and broad gene regulatory networks (GRNs), Schizophrenia risk genes in GRNs",, +ZNF263,99.0,Transcription factor linked to GRN from prefrontal cortex (PFC) - Broad cell types ,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5199 +MAZ,100.0,Transcription factor linked to GRN from prefrontal cortex (PFC) - Broad cell types ,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5200 +ZNF148,101.0,Transcription factor linked to GRN from prefrontal cortex (PFC) - Broad cell types ,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5201 +MEF2C,102.0,Transcription factor linked to GRN from prefrontal cortex (PFC) - Broad cell types ,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5202 +SP2,103.0,Transcription factor linked to GRN from prefrontal cortex (PFC) - Broad cell types ,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5203 +ZEB1,104.0,Transcription factor linked to GRN from prefrontal cortext (PFC) - more cell type specific,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5204 +PU2F2,105.0,Transcription factor linked to GRN from prefrontal cortext (PFC) - more cell type specific,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5205 +PPARA,106.0,Transcription factor linked to GRN from prefrontal cortext (PFC) - more cell type specific,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5206 +PBX3,107.0,Transcription factor linked to GRN from prefrontal cortext (PFC) - more cell type specific,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5207 +ELK4,108.0,Transcription factor linked to GRN from prefrontal cortext (PFC) - more cell type specific,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5208 +ETV6,109.0,Transcription factor linked to GRN from prefrontal cortext (PFC) - more cell type specific,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5209 +CLCN3,110.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +CNTN4,111.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +GATAD2A,112.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +GPM6A,113.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +MMP16,114.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +PSMA4,115.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +TCF4,116.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +NCAN,117.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +MAPK3,118.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +NMRAL1,119.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +CHRNB4,120.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +CHRNA3,121.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +CHRNA5,122.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +IREB2,123.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +PPP1R13B,124.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +BCL11B,125.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +PRKD1,126.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +OGFOD2,127.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +ATP2A2,128.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +SNX19,129.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +NRGN,130.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +DRD2,131.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +SERPING1,132.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +ZDHHC5,133.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +CACNB2,134.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +KCNV1,135.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +NNM16,136.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +SNAP91,137.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +GRIA1,138.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +PCDHA5,139.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +PCDHA8,140.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +HCN1,141.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +CLCN3,142.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +TMEM22,143.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +NEK4,144.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +PBRM1,145.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +ALMS1,146.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +VRK2,147.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +DUS2L,148.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +FURIN,149.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." +GRIN2A,150.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia." diff --git a/panel_design/README.md b/panel_design/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c1c37e8ed72d80f62aedf138b330691dbd4b5351 --- /dev/null +++ b/panel_design/README.md @@ -0,0 +1,25 @@ +# Panel design — human expert reference + +10 human scientists each designed a targeted gene panel for the human **dorsolateral +prefrontal cortex (DLPFC / PFC)**. Identities are removed; experts are numbered **1–10** +(this numbering is independent of the annotation task). Per-expert methodology is in +[`workflows.csv`](workflows.csv). + +## Files + +| File | Contents | +| --- | --- | +| `workflows.csv` | `id, workflow` — how each expert built their panel | +| `{1..10}.csv` | Full panel per expert: ranked gene list + rationale | +| `split/{id}_top{50,100,150}.csv` | Top-N subsets used for size-matched evaluation | + +## Columns + +Experts used different tools, so schemas are **not uniform**. Every panel includes a gene +symbol, a ranking/priority, and a free-text reasoning column; some include extra +tool-specific statistics (e.g. log fold-change, marker scores). Read each file on its own +terms rather than assuming a shared header. + +## Notes +- **Expert 3** submitted a previously designed panel for the wrong tissue (kept for completeness). +- Panels originally provided as `.xlsx` were converted to `.csv` unchanged. diff --git a/panel_design/split/10_top100.csv b/panel_design/split/10_top100.csv new file mode 100644 index 0000000000000000000000000000000000000000..847c80c75ac0f0079ac704562ddefb7c8af56278 --- /dev/null +++ b/panel_design/split/10_top100.csv @@ -0,0 +1,101 @@ +Unnamed: 0,Gene Symbol,Ranking,Annotation & Reasoning,Additional Comment +0,KCNIP4,top 50,Top DE genes for cell type - L2/3-6 intratelencephalic projecting glutamatergic neuron in the provided dataset, +1,R3HDM1,top 50,Top DE genes for cell type - L2/3-6 intratelencephalic projecting glutamatergic neuron in the provided dataset, +2,SATB2,top 50,Top DE genes for cell type - L2/3-6 intratelencephalic projecting glutamatergic neuron in the provided dataset, +3,VAT1L,top 50,Top DE genes for cell type - L5 extratelencephalic projecting glutamatergic cortical neuron in the provided dataset, +4,CLEC2L,top 50,Top DE genes for cell type - L5 extratelencephalic projecting glutamatergic cortical neuron in the provided dataset, +5,LMO7,top 50,Top DE genes for cell type - L5 extratelencephalic projecting glutamatergic cortical neuron in the provided dataset, +6,HS3ST4,top 50,Top DE genes for cell type - L6b glutamatergic cortical neuron in the provided dataset | Top DE genes for cell type - corticothalamic-projecting glutamatergic cortical neuron in the provided dataset | Top 20-50 HVG Genes, +7,ZFHX3,top 50,Top DE genes for cell type - L6b glutamatergic cortical neuron in the provided dataset, +8,TLE4,top 50,Top DE genes for cell type - L6b glutamatergic cortical neuron in the provided dataset, +9,ADGRV1,top 50,Top DE genes for cell type - astrocyte of the cerebral cortex in the provided dataset | Top 20 HVG Genes, +10,SLC1A3,top 50,Top DE genes for cell type - astrocyte of the cerebral cortex in the provided dataset | Top 20 HVG Genes, +11,SLC1A2,top 50,Top DE genes for cell type - astrocyte of the cerebral cortex in the provided dataset | Top 20 HVG Genes, +12,SORCS3,top 50,Top DE genes for cell type - caudal ganglionic eminence derived GABAergic cortical interneuron in the provided dataset, +13,ADARB2,top 50,Top DE genes for cell type - caudal ganglionic eminence derived GABAergic cortical interneuron in the provided dataset | Top DE genes for cell type - vip GABAergic cortical interneuron in the provided dataset | Top 20 HVG Genes, +14,CXCL14,top 50,"Top DE genes for cell type - caudal ganglionic eminence derived GABAergic cortical interneuron in the provided dataset | Top DE genes for cell type - sncg GABAergic cortical interneuron in the provided dataset | Top 20 HVG Genes | Top 2 Marker Genes for cell type - Neuroendocrine cells in human brain, according to PanglaoDB database", +15,ATP10A,top 50,Top DE genes for cell type - cerebral cortex endothelial cell in the provided dataset, +16,ABCB1,top 50,Top DE genes for cell type - cerebral cortex endothelial cell in the provided dataset, +17,MECOM,top 50,Top DE genes for cell type - cerebral cortex endothelial cell in the provided dataset, +18,CNTN5,top 50,Top DE genes for cell type - chandelier pvalb GABAergic cortical interneuron in the provided dataset, +19,ZNF385D,top 50,Top DE genes for cell type - chandelier pvalb GABAergic cortical interneuron in the provided dataset | Top 20-50 HVG Genes, +20,RORA,top 50,Top DE genes for cell type - chandelier pvalb GABAergic cortical interneuron in the provided dataset, +21,TRPM3,top 50,Top DE genes for cell type - corticothalamic-projecting glutamatergic cortical neuron in the provided dataset, +22,SEMA3E,top 50,Top DE genes for cell type - corticothalamic-projecting glutamatergic cortical neuron in the provided dataset, +23,FGF13,top 50,Top DE genes for cell type - lamp5 GABAergic cortical interneuron in the provided dataset, +24,FGF14,top 50,Top DE genes for cell type - lamp5 GABAergic cortical interneuron in the provided dataset, +25,MYO16,top 50,Top DE genes for cell type - lamp5 GABAergic cortical interneuron in the provided dataset, +26,PLXDC2,top 50,Top DE genes for cell type - microglial cell in the provided dataset | Top 20-50 HVG Genes, +27,DOCK4,top 50,Top DE genes for cell type - microglial cell in the provided dataset, +28,DOCK8,top 50,Top DE genes for cell type - microglial cell in the provided dataset | Top 20 HVG Genes, +29,NPSR1-AS1,top 50,Top DE genes for cell type - near-projecting glutamatergic cortical neuron in the provided dataset, +30,ASIC2,top 50,Top DE genes for cell type - near-projecting glutamatergic cortical neuron in the provided dataset, +31,ITGA8,top 50,Top DE genes for cell type - near-projecting glutamatergic cortical neuron in the provided dataset, +32,MBP,top 50,Top DE genes for cell type - oligodendrocyte in the provided dataset | Top 20 HVG Genes, +33,ST18,top 50,Top DE genes for cell type - oligodendrocyte in the provided dataset | Top 20 HVG Genes, +34,CTNNA3,top 50,Top DE genes for cell type - oligodendrocyte in the provided dataset | Top 20-50 HVG Genes, +35,LHFPL3,top 50,Top DE genes for cell type - oligodendrocyte precursor cell in the provided dataset | Top 20 HVG Genes, +36,DSCAM,top 50,Top DE genes for cell type - oligodendrocyte precursor cell in the provided dataset, +37,PTPRZ1,top 50,Top DE genes for cell type - oligodendrocyte precursor cell in the provided dataset, +38,PPARGC1A,top 50,Top DE genes for cell type - pvalb GABAergic cortical interneuron in the provided dataset, +39,FGF12,top 50,Top DE genes for cell type - pvalb GABAergic cortical interneuron in the provided dataset, +40,KCNC2,top 50,Top DE genes for cell type - pvalb GABAergic cortical interneuron in the provided dataset, +41,INPP4B,top 50,Top DE genes for cell type - sncg GABAergic cortical interneuron in the provided dataset, +42,FSTL5,top 50,Top DE genes for cell type - sncg GABAergic cortical interneuron in the provided dataset, +43,GRIK1,top 50,Top DE genes for cell type - sst GABAergic cortical interneuron in the provided dataset | Top 20 HVG Genes, +44,XKR4,top 50,Top DE genes for cell type - sst GABAergic cortical interneuron in the provided dataset, +45,KIAA1217,top 50,Top DE genes for cell type - sst GABAergic cortical interneuron in the provided dataset, +46,DLC1,top 50,Top DE genes for cell type - vascular leptomeningeal cell in the provided dataset, +47,ATP1A2,top 50,Top DE genes for cell type - vascular leptomeningeal cell in the provided dataset | Top 20-50 HVG Genes, +48,EBF1,top 50,Top DE genes for cell type - vascular leptomeningeal cell in the provided dataset | Top 20-50 HVG Genes, +49,RGS12,top 50,Top DE genes for cell type - vip GABAergic cortical interneuron in the provided dataset, +50,SYNPR,top 50-100,Top DE genes for cell type - vip GABAergic cortical interneuron in the provided dataset, +51,NPY,top 50-100,Top 20 HVG Genes, +52,ERBB4,top 50-100,Top 20 HVG Genes, +53,PLP1,top 50-100,Top 20 HVG Genes, +54,RELN,top 50-100,Top 20 HVG Genes, +55,CCL3,top 50-100,Top 20 HVG Genes, +56,GPC5,top 50-100,Top 20 HVG Genes, +57,SGCZ,top 50-100,Top 20 HVG Genes, +58,ARHGAP24,top 50-100,Top 20 HVG Genes, +59,RNF220,top 50-100,Top 20 HVG Genes, +60,APBB1IP,top 50-100,Top 20 HVG Genes, +61,SYT1,top 50-100,"Top 1 Marker Genes for cell type - Adrenergic neurons in human brain, according to PanglaoDB database", +62,NUCB2,top 50-100,"Top 1 Marker Genes for cell type - Anterior pituitary gland cells in human brain, according to PanglaoDB database", +63,VIM,top 50-100,"Top 1 Marker Genes for cell type - Astrocytes in human brain, according to PanglaoDB database | Top 1 Marker Genes for cell type - Bergmann glia in human brain, according to PanglaoDB database", +64,PABPN1,top 50-100,"Top 1 Marker Genes for cell type - Cajal-Retzius cells in human brain, according to PanglaoDB database", +65,ACLY,top 50-100,"Top 1 Marker Genes for cell type - Cholinergic neurons in human brain, according to PanglaoDB database", +66,TTR,top 50-100,"Top 1 Marker Genes for cell type - Choroid plexus cells in human brain, according to PanglaoDB database", +67,NR4A2,top 50-100,"Top 1 Marker Genes for cell type - Dopaminergic neurons in human brain, according to PanglaoDB database", +68,TM4SF1,top 50-100,"Top 1 Marker Genes for cell type - Ependymal cells in human brain, according to PanglaoDB database", +69,GADD45B,top 50-100,"Top 1 Marker Genes for cell type - GABAergic neurons in human brain, according to PanglaoDB database", +70,MEIS2,top 50-100,"Top 1 Marker Genes for cell type - Glutaminergic neurons in human brain, according to PanglaoDB database", +71,SLC32A1,top 50-100,"Top 1 Marker Genes for cell type - Glycinergic neurons in human brain, according to PanglaoDB database", +72,NES,top 50-100,"Top 1 Marker Genes for cell type - Immature neurons in human brain, according to PanglaoDB database", +73,RGS10,top 50-100,"Top 1 Marker Genes for cell type - Interneurons in human brain, according to PanglaoDB database", +74,IGFBP2,top 50-100,"Top 1 Marker Genes for cell type - Meningeal cells in human brain, according to PanglaoDB database", +75,FOS,top 50-100,"Top 1 Marker Genes for cell type - Microglia in human brain, according to PanglaoDB database", +76,ISL1,top 50-100,"Top 1 Marker Genes for cell type - Motor neurons in human brain, according to PanglaoDB database", +77,S100A6,top 50-100,"Top 1 Marker Genes for cell type - Neural stem/precursor cells in human brain, according to PanglaoDB database", +78,PBX1,top 50-100,"Top 1 Marker Genes for cell type - Neuroblasts in human brain, according to PanglaoDB database", +79,SST,top 50-100,"Top 1 Marker Genes for cell type - Neuroendocrine cells in human brain, according to PanglaoDB database | Top 2 Marker Genes for cell type - GABAergic neurons in human brain, according to PanglaoDB database", +80,PNISR,top 50-100,"Top 1 Marker Genes for cell type - Neurons in human brain, according to PanglaoDB database", +81,SLC9B2,top 50-100,"Top 1 Marker Genes for cell type - Noradrenergic neurons in human brain, according to PanglaoDB database", +82,VCAN,top 50-100,"Top 1 Marker Genes for cell type - Oligodendrocyte progenitor cells in human brain, according to PanglaoDB database | Top 20-50 HVG Genes", +83,GAMT,top 50-100,"Top 1 Marker Genes for cell type - Oligodendrocytes in human brain, according to PanglaoDB database", +84,CREM,top 50-100,"Top 1 Marker Genes for cell type - Pinealocytes in human brain, according to PanglaoDB database", +85,CD3G,top 50-100,"Top 1 Marker Genes for cell type - Purkinje neurons in human brain, according to PanglaoDB database", +86,YWHAZ,top 50-100,"Top 1 Marker Genes for cell type - Pyramidal cells in human brain, according to PanglaoDB database", +87,SPRY1,top 50-100,"Top 1 Marker Genes for cell type - Radial glia cells in human brain, according to PanglaoDB database", +88,NARF,top 50-100,"Top 1 Marker Genes for cell type - Retinal ganglion cells in human brain, according to PanglaoDB database", +89,GLUL,top 50-100,"Top 1 Marker Genes for cell type - Satellite glial cells in human brain, according to PanglaoDB database", +90,STMN1,top 50-100,"Top 1 Marker Genes for cell type - Schwann cells in human brain, according to PanglaoDB database", +91,ESM1,top 50-100,"Top 1 Marker Genes for cell type - Serotonergic neurons in human brain, according to PanglaoDB database", +92,PRDX6,top 50-100,"Top 1 Marker Genes for cell type - Tanycytes in human brain, according to PanglaoDB database", +93,CPNE3,top 50-100,"Top 1 Marker Genes for cell type - Trigeminal neurons in human brain, according to PanglaoDB database", +94,DDC,top 50-100,"Top 2 Marker Genes for cell type - Adrenergic neurons in human brain, according to PanglaoDB database | Top 2 Marker Genes for cell type - Noradrenergic neurons in human brain, according to PanglaoDB database | Top 2 Marker Genes for cell type - Serotonergic neurons in human brain, according to PanglaoDB database", +95,NKTR,top 50-100,"Top 2 Marker Genes for cell type - Anterior pituitary gland cells in human brain, according to PanglaoDB database", +96,APOE,top 50-100,"Top 2 Marker Genes for cell type - Astrocytes in human brain, according to PanglaoDB database", +97,ITGB1,top 50-100,"Top 2 Marker Genes for cell type - Bergmann glia in human brain, according to PanglaoDB database", +98,SLC25A36,top 50-100,"Top 2 Marker Genes for cell type - Cajal-Retzius cells in human brain, according to PanglaoDB database", +99,BRCA1,top 50-100,"Top 2 Marker Genes for cell type - Cholinergic neurons in human brain, according to PanglaoDB database", diff --git a/panel_design/split/10_top150.csv b/panel_design/split/10_top150.csv new file mode 100644 index 0000000000000000000000000000000000000000..519d6b156eed48fa186ae70a56718939654dd8dc --- /dev/null +++ b/panel_design/split/10_top150.csv @@ -0,0 +1,151 @@ +Unnamed: 0,Gene Symbol,Ranking,Annotation & Reasoning,Additional Comment +0,KCNIP4,top 50,Top DE genes for cell type - L2/3-6 intratelencephalic projecting glutamatergic neuron in the provided dataset, +1,R3HDM1,top 50,Top DE genes for cell type - L2/3-6 intratelencephalic projecting glutamatergic neuron in the provided dataset, +2,SATB2,top 50,Top DE genes for cell type - L2/3-6 intratelencephalic projecting glutamatergic neuron in the provided dataset, +3,VAT1L,top 50,Top DE genes for cell type - L5 extratelencephalic projecting glutamatergic cortical neuron in the provided dataset, +4,CLEC2L,top 50,Top DE genes for cell type - L5 extratelencephalic projecting glutamatergic cortical neuron in the provided dataset, +5,LMO7,top 50,Top DE genes for cell type - L5 extratelencephalic projecting glutamatergic cortical neuron in the provided dataset, +6,HS3ST4,top 50,Top DE genes for cell type - L6b glutamatergic cortical neuron in the provided dataset | Top DE genes for cell type - corticothalamic-projecting glutamatergic cortical neuron in the provided dataset | Top 20-50 HVG Genes, +7,ZFHX3,top 50,Top DE genes for cell type - L6b glutamatergic cortical neuron in the provided dataset, +8,TLE4,top 50,Top DE genes for cell type - L6b glutamatergic cortical neuron in the provided dataset, +9,ADGRV1,top 50,Top DE genes for cell type - astrocyte of the cerebral cortex in the provided dataset | Top 20 HVG Genes, +10,SLC1A3,top 50,Top DE genes for cell type - astrocyte of the cerebral cortex in the provided dataset | Top 20 HVG Genes, +11,SLC1A2,top 50,Top DE genes for cell type - astrocyte of the cerebral cortex in the provided dataset | Top 20 HVG Genes, +12,SORCS3,top 50,Top DE genes for cell type - caudal ganglionic eminence derived GABAergic cortical interneuron in the provided dataset, +13,ADARB2,top 50,Top DE genes for cell type - caudal ganglionic eminence derived GABAergic cortical interneuron in the provided dataset | Top DE genes for cell type - vip GABAergic cortical interneuron in the provided dataset | Top 20 HVG Genes, +14,CXCL14,top 50,"Top DE genes for cell type - caudal ganglionic eminence derived GABAergic cortical interneuron in the provided dataset | Top DE genes for cell type - sncg GABAergic cortical interneuron in the provided dataset | Top 20 HVG Genes | Top 2 Marker Genes for cell type - Neuroendocrine cells in human brain, according to PanglaoDB database", +15,ATP10A,top 50,Top DE genes for cell type - cerebral cortex endothelial cell in the provided dataset, +16,ABCB1,top 50,Top DE genes for cell type - cerebral cortex endothelial cell in the provided dataset, +17,MECOM,top 50,Top DE genes for cell type - cerebral cortex endothelial cell in the provided dataset, +18,CNTN5,top 50,Top DE genes for cell type - chandelier pvalb GABAergic cortical interneuron in the provided dataset, +19,ZNF385D,top 50,Top DE genes for cell type - chandelier pvalb GABAergic cortical interneuron in the provided dataset | Top 20-50 HVG Genes, +20,RORA,top 50,Top DE genes for cell type - chandelier pvalb GABAergic cortical interneuron in the provided dataset, +21,TRPM3,top 50,Top DE genes for cell type - corticothalamic-projecting glutamatergic cortical neuron in the provided dataset, +22,SEMA3E,top 50,Top DE genes for cell type - corticothalamic-projecting glutamatergic cortical neuron in the provided dataset, +23,FGF13,top 50,Top DE genes for cell type - lamp5 GABAergic cortical interneuron in the provided dataset, +24,FGF14,top 50,Top DE genes for cell type - lamp5 GABAergic cortical interneuron in the provided dataset, +25,MYO16,top 50,Top DE genes for cell type - lamp5 GABAergic cortical interneuron in the provided dataset, +26,PLXDC2,top 50,Top DE genes for cell type - microglial cell in the provided dataset | Top 20-50 HVG Genes, +27,DOCK4,top 50,Top DE genes for cell type - microglial cell in the provided dataset, +28,DOCK8,top 50,Top DE genes for cell type - microglial cell in the provided dataset | Top 20 HVG Genes, +29,NPSR1-AS1,top 50,Top DE genes for cell type - near-projecting glutamatergic cortical neuron in the provided dataset, +30,ASIC2,top 50,Top DE genes for cell type - near-projecting glutamatergic cortical neuron in the provided dataset, +31,ITGA8,top 50,Top DE genes for cell type - near-projecting glutamatergic cortical neuron in the provided dataset, +32,MBP,top 50,Top DE genes for cell type - oligodendrocyte in the provided dataset | Top 20 HVG Genes, +33,ST18,top 50,Top DE genes for cell type - oligodendrocyte in the provided dataset | Top 20 HVG Genes, +34,CTNNA3,top 50,Top DE genes for cell type - oligodendrocyte in the provided dataset | Top 20-50 HVG Genes, +35,LHFPL3,top 50,Top DE genes for cell type - oligodendrocyte precursor cell in the provided dataset | Top 20 HVG Genes, +36,DSCAM,top 50,Top DE genes for cell type - oligodendrocyte precursor cell in the provided dataset, +37,PTPRZ1,top 50,Top DE genes for cell type - oligodendrocyte precursor cell in the provided dataset, +38,PPARGC1A,top 50,Top DE genes for cell type - pvalb GABAergic cortical interneuron in the provided dataset, +39,FGF12,top 50,Top DE genes for cell type - pvalb GABAergic cortical interneuron in the provided dataset, +40,KCNC2,top 50,Top DE genes for cell type - pvalb GABAergic cortical interneuron in the provided dataset, +41,INPP4B,top 50,Top DE genes for cell type - sncg GABAergic cortical interneuron in the provided dataset, +42,FSTL5,top 50,Top DE genes for cell type - sncg GABAergic cortical interneuron in the provided dataset, +43,GRIK1,top 50,Top DE genes for cell type - sst GABAergic cortical interneuron in the provided dataset | Top 20 HVG Genes, +44,XKR4,top 50,Top DE genes for cell type - sst GABAergic cortical interneuron in the provided dataset, +45,KIAA1217,top 50,Top DE genes for cell type - sst GABAergic cortical interneuron in the provided dataset, +46,DLC1,top 50,Top DE genes for cell type - vascular leptomeningeal cell in the provided dataset, +47,ATP1A2,top 50,Top DE genes for cell type - vascular leptomeningeal cell in the provided dataset | Top 20-50 HVG Genes, +48,EBF1,top 50,Top DE genes for cell type - vascular leptomeningeal cell in the provided dataset | Top 20-50 HVG Genes, +49,RGS12,top 50,Top DE genes for cell type - vip GABAergic cortical interneuron in the provided dataset, +50,SYNPR,top 50-100,Top DE genes for cell type - vip GABAergic cortical interneuron in the provided dataset, +51,NPY,top 50-100,Top 20 HVG Genes, +52,ERBB4,top 50-100,Top 20 HVG Genes, +53,PLP1,top 50-100,Top 20 HVG Genes, +54,RELN,top 50-100,Top 20 HVG Genes, +55,CCL3,top 50-100,Top 20 HVG Genes, +56,GPC5,top 50-100,Top 20 HVG Genes, +57,SGCZ,top 50-100,Top 20 HVG Genes, +58,ARHGAP24,top 50-100,Top 20 HVG Genes, +59,RNF220,top 50-100,Top 20 HVG Genes, +60,APBB1IP,top 50-100,Top 20 HVG Genes, +61,SYT1,top 50-100,"Top 1 Marker Genes for cell type - Adrenergic neurons in human brain, according to PanglaoDB database", +62,NUCB2,top 50-100,"Top 1 Marker Genes for cell type - Anterior pituitary gland cells in human brain, according to PanglaoDB database", +63,VIM,top 50-100,"Top 1 Marker Genes for cell type - Astrocytes in human brain, according to PanglaoDB database | Top 1 Marker Genes for cell type - Bergmann glia in human brain, according to PanglaoDB database", +64,PABPN1,top 50-100,"Top 1 Marker Genes for cell type - Cajal-Retzius cells in human brain, according to PanglaoDB database", +65,ACLY,top 50-100,"Top 1 Marker Genes for cell type - Cholinergic neurons in human brain, according to PanglaoDB database", +66,TTR,top 50-100,"Top 1 Marker Genes for cell type - Choroid plexus cells in human brain, according to PanglaoDB database", +67,NR4A2,top 50-100,"Top 1 Marker Genes for cell type - Dopaminergic neurons in human brain, according to PanglaoDB database", +68,TM4SF1,top 50-100,"Top 1 Marker Genes for cell type - Ependymal cells in human brain, according to PanglaoDB database", +69,GADD45B,top 50-100,"Top 1 Marker Genes for cell type - GABAergic neurons in human brain, according to PanglaoDB database", +70,MEIS2,top 50-100,"Top 1 Marker Genes for cell type - Glutaminergic neurons in human brain, according to PanglaoDB database", +71,SLC32A1,top 50-100,"Top 1 Marker Genes for cell type - Glycinergic neurons in human brain, according to PanglaoDB database", +72,NES,top 50-100,"Top 1 Marker Genes for cell type - Immature neurons in human brain, according to PanglaoDB database", +73,RGS10,top 50-100,"Top 1 Marker Genes for cell type - Interneurons in human brain, according to PanglaoDB database", +74,IGFBP2,top 50-100,"Top 1 Marker Genes for cell type - Meningeal cells in human brain, according to PanglaoDB database", +75,FOS,top 50-100,"Top 1 Marker Genes for cell type - Microglia in human brain, according to PanglaoDB database", +76,ISL1,top 50-100,"Top 1 Marker Genes for cell type - Motor neurons in human brain, according to PanglaoDB database", +77,S100A6,top 50-100,"Top 1 Marker Genes for cell type - Neural stem/precursor cells in human brain, according to PanglaoDB database", +78,PBX1,top 50-100,"Top 1 Marker Genes for cell type - Neuroblasts in human brain, according to PanglaoDB database", +79,SST,top 50-100,"Top 1 Marker Genes for cell type - Neuroendocrine cells in human brain, according to PanglaoDB database | Top 2 Marker Genes for cell type - GABAergic neurons in human brain, according to PanglaoDB database", +80,PNISR,top 50-100,"Top 1 Marker Genes for cell type - Neurons in human brain, according to PanglaoDB database", +81,SLC9B2,top 50-100,"Top 1 Marker Genes for cell type - Noradrenergic neurons in human brain, according to PanglaoDB database", +82,VCAN,top 50-100,"Top 1 Marker Genes for cell type - Oligodendrocyte progenitor cells in human brain, according to PanglaoDB database | Top 20-50 HVG Genes", +83,GAMT,top 50-100,"Top 1 Marker Genes for cell type - Oligodendrocytes in human brain, according to PanglaoDB database", +84,CREM,top 50-100,"Top 1 Marker Genes for cell type - Pinealocytes in human brain, according to PanglaoDB database", +85,CD3G,top 50-100,"Top 1 Marker Genes for cell type - Purkinje neurons in human brain, according to PanglaoDB database", +86,YWHAZ,top 50-100,"Top 1 Marker Genes for cell type - Pyramidal cells in human brain, according to PanglaoDB database", +87,SPRY1,top 50-100,"Top 1 Marker Genes for cell type - Radial glia cells in human brain, according to PanglaoDB database", +88,NARF,top 50-100,"Top 1 Marker Genes for cell type - Retinal ganglion cells in human brain, according to PanglaoDB database", +89,GLUL,top 50-100,"Top 1 Marker Genes for cell type - Satellite glial cells in human brain, according to PanglaoDB database", +90,STMN1,top 50-100,"Top 1 Marker Genes for cell type - Schwann cells in human brain, according to PanglaoDB database", +91,ESM1,top 50-100,"Top 1 Marker Genes for cell type - Serotonergic neurons in human brain, according to PanglaoDB database", +92,PRDX6,top 50-100,"Top 1 Marker Genes for cell type - Tanycytes in human brain, according to PanglaoDB database", +93,CPNE3,top 50-100,"Top 1 Marker Genes for cell type - Trigeminal neurons in human brain, according to PanglaoDB database", +94,DDC,top 50-100,"Top 2 Marker Genes for cell type - Adrenergic neurons in human brain, according to PanglaoDB database | Top 2 Marker Genes for cell type - Noradrenergic neurons in human brain, according to PanglaoDB database | Top 2 Marker Genes for cell type - Serotonergic neurons in human brain, according to PanglaoDB database", +95,NKTR,top 50-100,"Top 2 Marker Genes for cell type - Anterior pituitary gland cells in human brain, according to PanglaoDB database", +96,APOE,top 50-100,"Top 2 Marker Genes for cell type - Astrocytes in human brain, according to PanglaoDB database", +97,ITGB1,top 50-100,"Top 2 Marker Genes for cell type - Bergmann glia in human brain, according to PanglaoDB database", +98,SLC25A36,top 50-100,"Top 2 Marker Genes for cell type - Cajal-Retzius cells in human brain, according to PanglaoDB database", +99,BRCA1,top 50-100,"Top 2 Marker Genes for cell type - Cholinergic neurons in human brain, according to PanglaoDB database", +100,CHMP1A,top 100-150,"Top 2 Marker Genes for cell type - Choroid plexus cells in human brain, according to PanglaoDB database", +101,SMAD3,top 100-150,"Top 2 Marker Genes for cell type - Dopaminergic neurons in human brain, according to PanglaoDB database", +102,RARRES2,top 100-150,"Top 2 Marker Genes for cell type - Ependymal cells in human brain, according to PanglaoDB database", +103,GLS,top 100-150,"Top 2 Marker Genes for cell type - Glutaminergic neurons in human brain, according to PanglaoDB database", +104,SLC6A9,top 100-150,"Top 2 Marker Genes for cell type - Glycinergic neurons in human brain, according to PanglaoDB database", +105,CREB1,top 100-150,"Top 2 Marker Genes for cell type - Immature neurons in human brain, according to PanglaoDB database", +106,DHRS3,top 100-150,"Top 2 Marker Genes for cell type - Interneurons in human brain, according to PanglaoDB database", +107,DCN,top 100-150,"Top 2 Marker Genes for cell type - Meningeal cells in human brain, according to PanglaoDB database", +108,EGR1,top 100-150,"Top 2 Marker Genes for cell type - Microglia in human brain, according to PanglaoDB database", +109,NKX6-1,top 100-150,"Top 2 Marker Genes for cell type - Motor neurons in human brain, according to PanglaoDB database", +110,RBM3,top 100-150,"Top 2 Marker Genes for cell type - Neural stem/precursor cells in human brain, according to PanglaoDB database", +111,EZH2,top 100-150,"Top 2 Marker Genes for cell type - Neuroblasts in human brain, according to PanglaoDB database", +112,MEG3,top 100-150,"Top 2 Marker Genes for cell type - Neurons in human brain, according to PanglaoDB database", +113,CNP,top 100-150,"Top 2 Marker Genes for cell type - Oligodendrocyte progenitor cells in human brain, according to PanglaoDB database", +114,PTGDS,top 100-150,"Top 2 Marker Genes for cell type - Oligodendrocytes in human brain, according to PanglaoDB database | Top 20-50 HVG Genes", +115,PMEPA1,top 100-150,"Top 2 Marker Genes for cell type - Pinealocytes in human brain, according to PanglaoDB database", +116,MRPS35,top 100-150,"Top 2 Marker Genes for cell type - Purkinje neurons in human brain, according to PanglaoDB database", +117,RTN4,top 100-150,"Top 2 Marker Genes for cell type - Pyramidal cells in human brain, according to PanglaoDB database", +118,PAX6,top 100-150,"Top 2 Marker Genes for cell type - Radial glia cells in human brain, according to PanglaoDB database", +119,RBPMS,top 100-150,"Top 2 Marker Genes for cell type - Retinal ganglion cells in human brain, according to PanglaoDB database", +120,CXCL8,top 100-150,"Top 2 Marker Genes for cell type - Satellite glial cells in human brain, according to PanglaoDB database", +121,SEPT9,top 100-150,"Top 2 Marker Genes for cell type - Schwann cells in human brain, according to PanglaoDB database", +122,RGCC,top 100-150,"Top 2 Marker Genes for cell type - Tanycytes in human brain, according to PanglaoDB database", +123,DHCR24,top 100-150,"Top 2 Marker Genes for cell type - Trigeminal neurons in human brain, according to PanglaoDB database", +124,HERC2P3_ENSG00000180229,top 100-150,Top 20-50 HVG Genes, +125,CLDN5,top 100-150,Top 20-50 HVG Genes, +126,GFAP,top 100-150,Top 20-50 HVG Genes, +127,OBI1-AS1,top 100-150,Top 20-50 HVG Genes, +128,QKI,top 100-150,Top 20-50 HVG Genes, +129,CCL4,top 100-150,Top 20-50 HVG Genes, +130,MOBP,top 100-150,Top 20-50 HVG Genes, +131,MT-CO3,top 100-150,Top 20-50 HVG Genes, +132,SPP1,top 100-150,Top 20-50 HVG Genes, +133,NXPH1,top 100-150,Top 20-50 HVG Genes, +134,FAM177B,top 100-150,Top 20-50 HVG Genes, +135,HPSE2,top 100-150,Top 20-50 HVG Genes, +136,ZBTB20,top 100-150,Top 20-50 HVG Genes, +137,ID3,top 100-150,Top 20-50 HVG Genes, +138,HSPA1A,top 100-150,Top 20-50 HVG Genes, +139,CCK,top 100-150,Top 20-50 HVG Genes, +140,PDE4B,top 100-150,Top 20-50 HVG Genes, +141,SOX2-OT,top 100-150,Top 20-50 HVG Genes, +142,HTR2C,top 100-150,Top 20-50 HVG Genes, +143,CERCAM,top 100-150,Top 20-50 HVG Genes, +144,PIP4K2A,top 100-150,Top 20-50 HVG Genes, +145,COLEC12,top 100-150,Top 20-50 HVG Genes, +146,CX3CR1,top 100-150,Top 20-50 HVG Genes, +147,PCDH15,top 100-150,Top 20-50 HVG Genes, +148,PRELID2,top 100-150,Top 20-50 HVG Genes, +149,FBXL7,top 100-150,Top 20-50 HVG Genes, diff --git a/panel_design/split/10_top50.csv b/panel_design/split/10_top50.csv new file mode 100644 index 0000000000000000000000000000000000000000..2293986b8beb692231eacd38ca69b081592abc3a --- /dev/null +++ b/panel_design/split/10_top50.csv @@ -0,0 +1,51 @@ +Unnamed: 0,Gene Symbol,Ranking,Annotation & Reasoning,Additional Comment +0,KCNIP4,top 50,Top DE genes for cell type - L2/3-6 intratelencephalic projecting glutamatergic neuron in the provided dataset, +1,R3HDM1,top 50,Top DE genes for cell type - L2/3-6 intratelencephalic projecting glutamatergic neuron in the provided dataset, +2,SATB2,top 50,Top DE genes for cell type - L2/3-6 intratelencephalic projecting glutamatergic neuron in the provided dataset, +3,VAT1L,top 50,Top DE genes for cell type - L5 extratelencephalic projecting glutamatergic cortical neuron in the provided dataset, +4,CLEC2L,top 50,Top DE genes for cell type - L5 extratelencephalic projecting glutamatergic cortical neuron in the provided dataset, +5,LMO7,top 50,Top DE genes for cell type - L5 extratelencephalic projecting glutamatergic cortical neuron in the provided dataset, +6,HS3ST4,top 50,Top DE genes for cell type - L6b glutamatergic cortical neuron in the provided dataset | Top DE genes for cell type - corticothalamic-projecting glutamatergic cortical neuron in the provided dataset | Top 20-50 HVG Genes, +7,ZFHX3,top 50,Top DE genes for cell type - L6b glutamatergic cortical neuron in the provided dataset, +8,TLE4,top 50,Top DE genes for cell type - L6b glutamatergic cortical neuron in the provided dataset, +9,ADGRV1,top 50,Top DE genes for cell type - astrocyte of the cerebral cortex in the provided dataset | Top 20 HVG Genes, +10,SLC1A3,top 50,Top DE genes for cell type - astrocyte of the cerebral cortex in the provided dataset | Top 20 HVG Genes, +11,SLC1A2,top 50,Top DE genes for cell type - astrocyte of the cerebral cortex in the provided dataset | Top 20 HVG Genes, +12,SORCS3,top 50,Top DE genes for cell type - caudal ganglionic eminence derived GABAergic cortical interneuron in the provided dataset, +13,ADARB2,top 50,Top DE genes for cell type - caudal ganglionic eminence derived GABAergic cortical interneuron in the provided dataset | Top DE genes for cell type - vip GABAergic cortical interneuron in the provided dataset | Top 20 HVG Genes, +14,CXCL14,top 50,"Top DE genes for cell type - caudal ganglionic eminence derived GABAergic cortical interneuron in the provided dataset | Top DE genes for cell type - sncg GABAergic cortical interneuron in the provided dataset | Top 20 HVG Genes | Top 2 Marker Genes for cell type - Neuroendocrine cells in human brain, according to PanglaoDB database", +15,ATP10A,top 50,Top DE genes for cell type - cerebral cortex endothelial cell in the provided dataset, +16,ABCB1,top 50,Top DE genes for cell type - cerebral cortex endothelial cell in the provided dataset, +17,MECOM,top 50,Top DE genes for cell type - cerebral cortex endothelial cell in the provided dataset, +18,CNTN5,top 50,Top DE genes for cell type - chandelier pvalb GABAergic cortical interneuron in the provided dataset, +19,ZNF385D,top 50,Top DE genes for cell type - chandelier pvalb GABAergic cortical interneuron in the provided dataset | Top 20-50 HVG Genes, +20,RORA,top 50,Top DE genes for cell type - chandelier pvalb GABAergic cortical interneuron in the provided dataset, +21,TRPM3,top 50,Top DE genes for cell type - corticothalamic-projecting glutamatergic cortical neuron in the provided dataset, +22,SEMA3E,top 50,Top DE genes for cell type - corticothalamic-projecting glutamatergic cortical neuron in the provided dataset, +23,FGF13,top 50,Top DE genes for cell type - lamp5 GABAergic cortical interneuron in the provided dataset, +24,FGF14,top 50,Top DE genes for cell type - lamp5 GABAergic cortical interneuron in the provided dataset, +25,MYO16,top 50,Top DE genes for cell type - lamp5 GABAergic cortical interneuron in the provided dataset, +26,PLXDC2,top 50,Top DE genes for cell type - microglial cell in the provided dataset | Top 20-50 HVG Genes, +27,DOCK4,top 50,Top DE genes for cell type - microglial cell in the provided dataset, +28,DOCK8,top 50,Top DE genes for cell type - microglial cell in the provided dataset | Top 20 HVG Genes, +29,NPSR1-AS1,top 50,Top DE genes for cell type - near-projecting glutamatergic cortical neuron in the provided dataset, +30,ASIC2,top 50,Top DE genes for cell type - near-projecting glutamatergic cortical neuron in the provided dataset, +31,ITGA8,top 50,Top DE genes for cell type - near-projecting glutamatergic cortical neuron in the provided dataset, +32,MBP,top 50,Top DE genes for cell type - oligodendrocyte in the provided dataset | Top 20 HVG Genes, +33,ST18,top 50,Top DE genes for cell type - oligodendrocyte in the provided dataset | Top 20 HVG Genes, +34,CTNNA3,top 50,Top DE genes for cell type - oligodendrocyte in the provided dataset | Top 20-50 HVG Genes, +35,LHFPL3,top 50,Top DE genes for cell type - oligodendrocyte precursor cell in the provided dataset | Top 20 HVG Genes, +36,DSCAM,top 50,Top DE genes for cell type - oligodendrocyte precursor cell in the provided dataset, +37,PTPRZ1,top 50,Top DE genes for cell type - oligodendrocyte precursor cell in the provided dataset, +38,PPARGC1A,top 50,Top DE genes for cell type - pvalb GABAergic cortical interneuron in the provided dataset, +39,FGF12,top 50,Top DE genes for cell type - pvalb GABAergic cortical interneuron in the provided dataset, +40,KCNC2,top 50,Top DE genes for cell type - pvalb GABAergic cortical interneuron in the provided dataset, +41,INPP4B,top 50,Top DE genes for cell type - sncg GABAergic cortical interneuron in the provided dataset, +42,FSTL5,top 50,Top DE genes for cell type - sncg GABAergic cortical interneuron in the provided dataset, +43,GRIK1,top 50,Top DE genes for cell type - sst GABAergic cortical interneuron in the provided dataset | Top 20 HVG Genes, +44,XKR4,top 50,Top DE genes for cell type - sst GABAergic cortical interneuron in the provided dataset, +45,KIAA1217,top 50,Top DE genes for cell type - sst GABAergic cortical interneuron in the provided dataset, +46,DLC1,top 50,Top DE genes for cell type - vascular leptomeningeal cell in the provided dataset, +47,ATP1A2,top 50,Top DE genes for cell type - vascular leptomeningeal cell in the provided dataset | Top 20-50 HVG Genes, +48,EBF1,top 50,Top DE genes for cell type - vascular leptomeningeal cell in the provided dataset | Top 20-50 HVG Genes, +49,RGS12,top 50,Top DE genes for cell type - vip GABAergic cortical interneuron in the provided dataset, diff --git a/panel_design/split/1_top100.csv b/panel_design/split/1_top100.csv new file mode 100644 index 0000000000000000000000000000000000000000..bacb0769f23c5ee47dbd9f8358fdca5a408b79ea --- /dev/null +++ b/panel_design/split/1_top100.csv @@ -0,0 +1,101 @@ +Unnamed: 0,Gene symbol,Ranking,Annotation & reasoning,Additional note,Gene Symbol +0,NeuN,1-50,Pan Neuron marker often used for ISH,,NeuN +1,SST,1-50,Defines SST+ Interneurons,,SST +2,PVALB,1-50,Identifies inhibitory interneurons,,PVALB +3,CLND5,1-50,Endothelial cells / Mural cells,,CLND5 +4,HBA1,1-50,Endothelial cells / Mural cells,,HBA1 +5,ASCA2,1-50,Astrocyte marker often used for Bead collection,,ASCA2 +6,GFAP,1-50,Astrocyte marker ,,GFAP +7,CX3CR1 ,1-50,Microglia marker,,CX3CR1 +8,TMEM119,1-50,Microglial marker,,TMEM119 +9,AIF1,1-50,IBA1 is often used for in situ hybridzation to label microglial cells. ,,AIF1 +10,OLIG2,1-50,"Expressed by OPCs, getting cells ready for differentiation into myelin-forming oligodendocytes. ",,OLIG2 +11,CD22,1-50,Expressed by oligodendrocytes in huamns and binds to sialic acid-dependent ligands on microglia. ,,CD22 +12,Th,1-50,Often used by ISH of dopaminergic neurons. ,,Th +13,Reln,1-50,"Genes that seperate layers of cortical region (Huuki-Myers, BioRxiv, 2023)",,Reln +14,Aqp4,1-50,"Genes that seperate layers of cortical region (Huuki-Myers, BioRxiv, 2023)",,Aqp4 +15,SPARC,1-50,"Genes that seperate layers of cortical region (Huuki-Myers, BioRxiv, 2023)",,SPARC +16,HTRA1,1-50,"Genes that seperate layers of cortical region (Huuki-Myers, BioRxiv, 2023)",,HTRA1 +17,VIP,1-50,Labels interneurons in PFC that signal and inhibits SST+ interneurons,,VIP +18,Fos,1-50,Activation-related genes from neurons. ,,Fos +19,Arc,1-50,Activation-related genes from neurons. ,,Arc +20,Egr1,1-50,Activation-related genes from neurons. ,,Egr1 +21,BDNF,1-50,"For survival mechanisms of neuronal homeostasis, often associated with disease. ",,BDNF +22,ADORA1,1-50,Neurotransmitter receptors,,ADORA1 +23,HTR1A,1-50,Neurotransmitter receptors,,HTR1A +24,HTR2A,1-50,Neurotransmitter receptors,,HTR2A +25,HTR3A,1-50,Neurotransmitter receptors,,HTR3A +26,HTR4,1-50,Neurotransmitter receptors,,HTR4 +27,DRD1,1-50,Neurotransmitter receptors,,DRD1 +28,DRD2,1-50,Neurotransmitter receptors,,DRD2 +29,DRD4,1-50,Neurotransmitter receptors,,DRD4 +30,NR3C1,1-50,Neurotransmitter receptors,,NR3C1 +31,NPY1R,1-50,Neurotransmitter receptors,,NPY1R +32,OXTR,1-50,Expressed by SST+ neurons to respond to ,,OXTR +33,GRIN2B,1-50,Receptors common for neural plasticity,,GRIN2B +34,GABRA1,1-50,Receptors common for neural plasticity,,GABRA1 +35,GRIA1,1-50,Receptors common for neural plasticity,,GRIA1 +36,NEDD4,1-50,Marker for excitatory neurons,,NEDD4 +37,FBXO2,1-50,Marker for excitatory neurons,,FBXO2 +38,mTOR,1-50,Marker for excitatory neurons,,mTOR +39,DDIT4,1-50,Marker for excitatory neurons,,DDIT4 +40,TH,1-50,Marker for excitatory neurons,,TH +41,PDGFRA,1-50,OPCs,,PDGFRA +42,GAD1,1-50,"Glutamate Decarboxylase 1, catalyzing production from L-glut. ",,GAD1 +43,CHAT,1-50,Neuron enzyme for ACh,,CHAT +44,GRIN2A,1-50,NMDA receptors,,GRIN2A +45,GABRD,1-50,GABA receptors,,GABRD +46,GABRA1,1-50,GABA receptors,,GABRA1 +47,TREM2,1-50,microglial marker,,TREM2 +48,CSF1R,1-50,microglial marker,,CSF1R +49,IL10,1-50,Microglia function,,IL10 +50,EFNA5,50-100,Important pathways for neural plasticity and synaptic homeostasis. ,,EFNA5 +51,EPHA5,50-100,Important pathways for neural plasticity and synaptic homeostasis. ,,EPHA5 +52,FYN,50-100,Important pathways for neural plasticity and synaptic homeostasis. ,,FYN +53,CARMN,50-100,Endothelial cells: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,CARMN +54,ITIH5,50-100,Endothelial cells: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,ITIH5 +55,MECOM,50-100,Endothelial cells: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,MECOM +56,EBF1,50-100,Endothelial cells: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,EBF1 +57,VWF,50-100,Endothelial cells: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,VWF +58,LINC02712,50-100,Microglia: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,LINC02712 +59,ITGAX,50-100,Microglia: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,ITGAX +60,BLNK,50-100,Microglia: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,BLNK +61,CSF2RA,50-100,Microglia: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,CSF2RA +62,FOLH1,50-100,Oligodendrocytes: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,FOLH1 +63,LINC01608,50-100,Oligodendrocytes: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,LINC01608 +64,SLC5A11,50-100,Oligodendrocytes: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,SLC5A11 +65,OPC,50-100,OPC: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,OPC +66,AC004852.2,50-100,OPC: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,AC004852.2 +67,FERMT1,50-100,OPC: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,FERMT1 +68,COL9A1,50-100,OPC: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,COL9A1 +69,STK32A,50-100,OPC: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,STK32A +70,FGF13,50-100,Excitatory Layer 2 or 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,FGF13 +71,SLC12A8,50-100,Excitatory Layer 2 or 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,SLC12A8 +72,DCBLD2,50-100,Excitatory Layer 2 or 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,DCBLD2 +73,MPC1,50-100,Excitatory Layer 2 or 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,MPC1 +74,LINC02296,50-100,Excitatory Layer 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,LINC02296 +75,AC008674.1,50-100,Excitatory Layer 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,AC008674.1 +76,CLRA3,50-100,Excitatory Layer 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,CLRA3 +77,CPHR1,50-100,Excitatory Layer 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,CPHR1 +78,FBXL16,50-100,Excitatory Layer 3/4/5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,FBXL16 +79,MAP1A,50-100,Excitatory Layer 3/4/5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,MAP1A +80,UBB,50-100,Excitatory Layer 3/4/5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,UBB +81,ENC1,50-100,Excitatory Layer 3/4/5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,ENC1 +82,TSHZ2,50-100,Excitatory Layer 4 Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,TSHZ2 +83,VGF,50-100,Excitatory Layer 4 Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,VGF +84,UBE2E3,50-100,Excitatory Layer 4 Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,UBE2E3 +85,APP003066.1,50-100,Excitatory Layer 5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,APP003066.1 +86,COL12A1,50-100,Excitatory Layer 5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,COL12A1 +87,TRABD2A,50-100,Excitatory Layer 5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,TRABD2A +88,TLL1,50-100,Excitatory Layer 5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,TLL1 +89,LINC00343,50-100,Excitatory L5/6,,LINC00343 +90,THEMIS,50-100,Excitatory L5/6,,THEMIS +91,AC015943.1,50-100,Excitatory L5/6,,AC015943.1 +92,LINC02718,50-100,Excitatory L6: Top genes from Huuki-Myers,,LINC02718 +93,MCTP2,50-100,Excitatory L6: Top genes from Huuki-Myers,,MCTP2 +94,AC006299.1,50-100,Excitatory L6: Top genes from Huuki-Myers,,AC006299.1 +95,DPP4,50-100,Excitatory L6: Top genes from Huuki-Myers,,DPP4 +96,MYO3B,50-100,Inhibitory neurons: Top genes from Huuki-Myers,,MYO3B +97,SLC27A6,50-100,Inhibitory neurons: Top genes from Huuki-Myers,,SLC27A6 +98,MINAR1,50-100,Inhibitory neurons: Top genes from Huuki-Myers,,MINAR1 +99,BTBD11,50-100,Inhibitory neurons: Top genes from Huuki-Myers,,BTBD11 diff --git a/panel_design/split/1_top150.csv b/panel_design/split/1_top150.csv new file mode 100644 index 0000000000000000000000000000000000000000..4d931247c484042f00ab516351621790bdeef279 --- /dev/null +++ b/panel_design/split/1_top150.csv @@ -0,0 +1,151 @@ +Unnamed: 0,Gene symbol,Ranking,Annotation & reasoning,Additional note,Gene Symbol +0,NeuN,1-50,Pan Neuron marker often used for ISH,,NeuN +1,SST,1-50,Defines SST+ Interneurons,,SST +2,PVALB,1-50,Identifies inhibitory interneurons,,PVALB +3,CLND5,1-50,Endothelial cells / Mural cells,,CLND5 +4,HBA1,1-50,Endothelial cells / Mural cells,,HBA1 +5,ASCA2,1-50,Astrocyte marker often used for Bead collection,,ASCA2 +6,GFAP,1-50,Astrocyte marker ,,GFAP +7,CX3CR1 ,1-50,Microglia marker,,CX3CR1 +8,TMEM119,1-50,Microglial marker,,TMEM119 +9,AIF1,1-50,IBA1 is often used for in situ hybridzation to label microglial cells. ,,AIF1 +10,OLIG2,1-50,"Expressed by OPCs, getting cells ready for differentiation into myelin-forming oligodendocytes. ",,OLIG2 +11,CD22,1-50,Expressed by oligodendrocytes in huamns and binds to sialic acid-dependent ligands on microglia. ,,CD22 +12,Th,1-50,Often used by ISH of dopaminergic neurons. ,,Th +13,Reln,1-50,"Genes that seperate layers of cortical region (Huuki-Myers, BioRxiv, 2023)",,Reln +14,Aqp4,1-50,"Genes that seperate layers of cortical region (Huuki-Myers, BioRxiv, 2023)",,Aqp4 +15,SPARC,1-50,"Genes that seperate layers of cortical region (Huuki-Myers, BioRxiv, 2023)",,SPARC +16,HTRA1,1-50,"Genes that seperate layers of cortical region (Huuki-Myers, BioRxiv, 2023)",,HTRA1 +17,VIP,1-50,Labels interneurons in PFC that signal and inhibits SST+ interneurons,,VIP +18,Fos,1-50,Activation-related genes from neurons. ,,Fos +19,Arc,1-50,Activation-related genes from neurons. ,,Arc +20,Egr1,1-50,Activation-related genes from neurons. ,,Egr1 +21,BDNF,1-50,"For survival mechanisms of neuronal homeostasis, often associated with disease. ",,BDNF +22,ADORA1,1-50,Neurotransmitter receptors,,ADORA1 +23,HTR1A,1-50,Neurotransmitter receptors,,HTR1A +24,HTR2A,1-50,Neurotransmitter receptors,,HTR2A +25,HTR3A,1-50,Neurotransmitter receptors,,HTR3A +26,HTR4,1-50,Neurotransmitter receptors,,HTR4 +27,DRD1,1-50,Neurotransmitter receptors,,DRD1 +28,DRD2,1-50,Neurotransmitter receptors,,DRD2 +29,DRD4,1-50,Neurotransmitter receptors,,DRD4 +30,NR3C1,1-50,Neurotransmitter receptors,,NR3C1 +31,NPY1R,1-50,Neurotransmitter receptors,,NPY1R +32,OXTR,1-50,Expressed by SST+ neurons to respond to ,,OXTR +33,GRIN2B,1-50,Receptors common for neural plasticity,,GRIN2B +34,GABRA1,1-50,Receptors common for neural plasticity,,GABRA1 +35,GRIA1,1-50,Receptors common for neural plasticity,,GRIA1 +36,NEDD4,1-50,Marker for excitatory neurons,,NEDD4 +37,FBXO2,1-50,Marker for excitatory neurons,,FBXO2 +38,mTOR,1-50,Marker for excitatory neurons,,mTOR +39,DDIT4,1-50,Marker for excitatory neurons,,DDIT4 +40,TH,1-50,Marker for excitatory neurons,,TH +41,PDGFRA,1-50,OPCs,,PDGFRA +42,GAD1,1-50,"Glutamate Decarboxylase 1, catalyzing production from L-glut. ",,GAD1 +43,CHAT,1-50,Neuron enzyme for ACh,,CHAT +44,GRIN2A,1-50,NMDA receptors,,GRIN2A +45,GABRD,1-50,GABA receptors,,GABRD +46,GABRA1,1-50,GABA receptors,,GABRA1 +47,TREM2,1-50,microglial marker,,TREM2 +48,CSF1R,1-50,microglial marker,,CSF1R +49,IL10,1-50,Microglia function,,IL10 +50,EFNA5,50-100,Important pathways for neural plasticity and synaptic homeostasis. ,,EFNA5 +51,EPHA5,50-100,Important pathways for neural plasticity and synaptic homeostasis. ,,EPHA5 +52,FYN,50-100,Important pathways for neural plasticity and synaptic homeostasis. ,,FYN +53,CARMN,50-100,Endothelial cells: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,CARMN +54,ITIH5,50-100,Endothelial cells: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,ITIH5 +55,MECOM,50-100,Endothelial cells: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,MECOM +56,EBF1,50-100,Endothelial cells: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,EBF1 +57,VWF,50-100,Endothelial cells: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,VWF +58,LINC02712,50-100,Microglia: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,LINC02712 +59,ITGAX,50-100,Microglia: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,ITGAX +60,BLNK,50-100,Microglia: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,BLNK +61,CSF2RA,50-100,Microglia: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,CSF2RA +62,FOLH1,50-100,Oligodendrocytes: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,FOLH1 +63,LINC01608,50-100,Oligodendrocytes: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,LINC01608 +64,SLC5A11,50-100,Oligodendrocytes: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,SLC5A11 +65,OPC,50-100,OPC: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,OPC +66,AC004852.2,50-100,OPC: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,AC004852.2 +67,FERMT1,50-100,OPC: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,FERMT1 +68,COL9A1,50-100,OPC: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,COL9A1 +69,STK32A,50-100,OPC: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,STK32A +70,FGF13,50-100,Excitatory Layer 2 or 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,FGF13 +71,SLC12A8,50-100,Excitatory Layer 2 or 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,SLC12A8 +72,DCBLD2,50-100,Excitatory Layer 2 or 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,DCBLD2 +73,MPC1,50-100,Excitatory Layer 2 or 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,MPC1 +74,LINC02296,50-100,Excitatory Layer 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,LINC02296 +75,AC008674.1,50-100,Excitatory Layer 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,AC008674.1 +76,CLRA3,50-100,Excitatory Layer 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,CLRA3 +77,CPHR1,50-100,Excitatory Layer 3: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,CPHR1 +78,FBXL16,50-100,Excitatory Layer 3/4/5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,FBXL16 +79,MAP1A,50-100,Excitatory Layer 3/4/5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,MAP1A +80,UBB,50-100,Excitatory Layer 3/4/5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,UBB +81,ENC1,50-100,Excitatory Layer 3/4/5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,ENC1 +82,TSHZ2,50-100,Excitatory Layer 4 Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,TSHZ2 +83,VGF,50-100,Excitatory Layer 4 Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,VGF +84,UBE2E3,50-100,Excitatory Layer 4 Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,UBE2E3 +85,APP003066.1,50-100,Excitatory Layer 5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,APP003066.1 +86,COL12A1,50-100,Excitatory Layer 5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,COL12A1 +87,TRABD2A,50-100,Excitatory Layer 5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,TRABD2A +88,TLL1,50-100,Excitatory Layer 5: Top genes from Huuki-Myers that distinguishes other cell types using scNuc-seq and spatial context. ,,TLL1 +89,LINC00343,50-100,Excitatory L5/6,,LINC00343 +90,THEMIS,50-100,Excitatory L5/6,,THEMIS +91,AC015943.1,50-100,Excitatory L5/6,,AC015943.1 +92,LINC02718,50-100,Excitatory L6: Top genes from Huuki-Myers,,LINC02718 +93,MCTP2,50-100,Excitatory L6: Top genes from Huuki-Myers,,MCTP2 +94,AC006299.1,50-100,Excitatory L6: Top genes from Huuki-Myers,,AC006299.1 +95,DPP4,50-100,Excitatory L6: Top genes from Huuki-Myers,,DPP4 +96,MYO3B,50-100,Inhibitory neurons: Top genes from Huuki-Myers,,MYO3B +97,SLC27A6,50-100,Inhibitory neurons: Top genes from Huuki-Myers,,SLC27A6 +98,MINAR1,50-100,Inhibitory neurons: Top genes from Huuki-Myers,,MINAR1 +99,BTBD11,50-100,Inhibitory neurons: Top genes from Huuki-Myers,,BTBD11 +100,FBN2,100-150,Inhibitory neurons: Top genes from Huuki-Myers,,FBN2 +101,GRIP2,100-150,Inhibitory neurons: Top genes from Huuki-Myers,,GRIP2 +102,COMT,100-150,Enzymes that degrade neurotransmitters,,COMT +103,SLC6A3,100-150,Dopamine transporter,,SLC6A3 +104,MAOA,100-150,Breakdown of neurotransmitters,,MAOA +105,CREB1,100-150,Neural activation related genes,,CREB1 +106,FOS,100-150,Neural activation related genes,,FOS +107,JUNB,100-150,Neural activation related genes,,JUNB +108,NFAT1,100-150,Neural activation related genes,,NFAT1 +109,CRTC1,100-150,Neural activation related genes,,CRTC1 +110,CAMK2A,100-150,Neural activation related genes,,CAMK2A +111,CAMK1D,100-150,Neural activation related genes,,CAMK1D +112,APOE4,100-150,"Alzhiemers, microglia. ",,APOE4 +113,SHANK3,100-150,Genes altered in ASD,,SHANK3 +114,RAC1,100-150,Genes altered in ASD,,RAC1 +115,PAK,100-150,Genes altered in ASD,,PAK +116,COFILIN,100-150,Genes altered in ASD,,COFILIN +117,NR2A,100-150,Genes altered in Schizophernia,,NR2A +118,GAD67,100-150,Genes altered in Schizophernia,,GAD67 +119,CALM2,100-150,Genes altered in MDD brains also associated with synatic function and reduced spine density in layers II/III of DLPFC,,CALM2 +120,SYN1,100-150,Genes altered in MDD brains also associated with synatic function and reduced spine density in layers II/III of DLPFC,,SYN1 +121,RAB3A,100-150,Genes altered in MDD brains also associated with synatic function and reduced spine density in layers II/III of DLPFC,,RAB3A +122,RAB4B,100-150,Genes altered in MDD brains also associated with synatic function and reduced spine density in layers II/III of DLPFC,,RAB4B +123,TUBB4,100-150,Genes altered in MDD brains also associated with synatic function and reduced spine density in layers II/III of DLPFC,,TUBB4 +124,NR2B,100-150,Genes altered in MDD brains also associated with synatic function and reduced spine density in layers II/III of DLPFC,,NR2B +125,PSD96,100-150,Genes altered in MDD brains also associated with synatic function and reduced spine density in layers II/III of DLPFC,,PSD96 +126,cpg15,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ",,cpg15 +127,NTRK2,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ",,NTRK2 +128,HLA-A,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ",,HLA-A +129,PLK2,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ",,PLK2 +130,Homer1,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ",,Homer1 +131,Arc,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ",,Arc +132,MIR134,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ",,MIR134 +133,Mecp2,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ",,Mecp2 +134,MEF22c,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ",,MEF22c +135,CARF,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ",,CARF +136,HLA-B,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ",,HLA-B +137,HLA-C,100-150,"Neuronal activity-regulated genes, that alter synaptic function of neurons. ",,HLA-C +138,KIT,100-150,Inhibitory neurons: Top genes from Huuki-Myers,,KIT +139,PLXDC2,100-150,Top DEG from Jupyter of microglia,,PLXDC2 +140,DOCK4,100-150,Top DEG from Jupyter of microglia,,DOCK4 +141,DOCK8,100-150,Top DEG from Jupyter of microglia,,DOCK8 +142,AdGRV1,100-150,Top DEG from jupyter of astrocytes,,AdGRV1 +143,SLC1A2,100-150,Top DEG from jupyter of astrocytes,,SLC1A2 +144,MSI2,100-150,Top DEG from jupyter of astrocytes,,MSI2 +145,GPC5,100-150,Top DEG from jupyter of astrocytes,,GPC5 +146,SORCS3,100-150,Caudal Ganglionic derived GABAergic cortical interneurons from Top DEG from Jupyter,,SORCS3 +147,ADARB2,100-150,Caudal Ganglionic derived GABAergic cortical interneurons from Top DEG from Jupyter,,ADARB2 +148,CXCL14,100-150,Caudal Ganglionic derived GABAergic cortical interneurons from Top DEG from Jupyter,,CXCL14 +149,SLC35F4,100-150,Caudal Ganglionic derived GABAergic cortical interneurons from Top DEG from Jupyter,,SLC35F4 diff --git a/panel_design/split/1_top50.csv b/panel_design/split/1_top50.csv new file mode 100644 index 0000000000000000000000000000000000000000..9d8a2f2752ef4d9fb8ae79bf31a3f895fcd19112 --- /dev/null +++ b/panel_design/split/1_top50.csv @@ -0,0 +1,51 @@ +Unnamed: 0,Gene symbol,Ranking,Annotation & reasoning,Additional note,Gene Symbol +0,NeuN,1-50,Pan Neuron marker often used for ISH,,NeuN +1,SST,1-50,Defines SST+ Interneurons,,SST +2,PVALB,1-50,Identifies inhibitory interneurons,,PVALB +3,CLND5,1-50,Endothelial cells / Mural cells,,CLND5 +4,HBA1,1-50,Endothelial cells / Mural cells,,HBA1 +5,ASCA2,1-50,Astrocyte marker often used for Bead collection,,ASCA2 +6,GFAP,1-50,Astrocyte marker ,,GFAP +7,CX3CR1 ,1-50,Microglia marker,,CX3CR1 +8,TMEM119,1-50,Microglial marker,,TMEM119 +9,AIF1,1-50,IBA1 is often used for in situ hybridzation to label microglial cells. ,,AIF1 +10,OLIG2,1-50,"Expressed by OPCs, getting cells ready for differentiation into myelin-forming oligodendocytes. ",,OLIG2 +11,CD22,1-50,Expressed by oligodendrocytes in huamns and binds to sialic acid-dependent ligands on microglia. ,,CD22 +12,Th,1-50,Often used by ISH of dopaminergic neurons. ,,Th +13,Reln,1-50,"Genes that seperate layers of cortical region (Huuki-Myers, BioRxiv, 2023)",,Reln +14,Aqp4,1-50,"Genes that seperate layers of cortical region (Huuki-Myers, BioRxiv, 2023)",,Aqp4 +15,SPARC,1-50,"Genes that seperate layers of cortical region (Huuki-Myers, BioRxiv, 2023)",,SPARC +16,HTRA1,1-50,"Genes that seperate layers of cortical region (Huuki-Myers, BioRxiv, 2023)",,HTRA1 +17,VIP,1-50,Labels interneurons in PFC that signal and inhibits SST+ interneurons,,VIP +18,Fos,1-50,Activation-related genes from neurons. ,,Fos +19,Arc,1-50,Activation-related genes from neurons. ,,Arc +20,Egr1,1-50,Activation-related genes from neurons. ,,Egr1 +21,BDNF,1-50,"For survival mechanisms of neuronal homeostasis, often associated with disease. ",,BDNF +22,ADORA1,1-50,Neurotransmitter receptors,,ADORA1 +23,HTR1A,1-50,Neurotransmitter receptors,,HTR1A +24,HTR2A,1-50,Neurotransmitter receptors,,HTR2A +25,HTR3A,1-50,Neurotransmitter receptors,,HTR3A +26,HTR4,1-50,Neurotransmitter receptors,,HTR4 +27,DRD1,1-50,Neurotransmitter receptors,,DRD1 +28,DRD2,1-50,Neurotransmitter receptors,,DRD2 +29,DRD4,1-50,Neurotransmitter receptors,,DRD4 +30,NR3C1,1-50,Neurotransmitter receptors,,NR3C1 +31,NPY1R,1-50,Neurotransmitter receptors,,NPY1R +32,OXTR,1-50,Expressed by SST+ neurons to respond to ,,OXTR +33,GRIN2B,1-50,Receptors common for neural plasticity,,GRIN2B +34,GABRA1,1-50,Receptors common for neural plasticity,,GABRA1 +35,GRIA1,1-50,Receptors common for neural plasticity,,GRIA1 +36,NEDD4,1-50,Marker for excitatory neurons,,NEDD4 +37,FBXO2,1-50,Marker for excitatory neurons,,FBXO2 +38,mTOR,1-50,Marker for excitatory neurons,,mTOR +39,DDIT4,1-50,Marker for excitatory neurons,,DDIT4 +40,TH,1-50,Marker for excitatory neurons,,TH +41,PDGFRA,1-50,OPCs,,PDGFRA +42,GAD1,1-50,"Glutamate Decarboxylase 1, catalyzing production from L-glut. ",,GAD1 +43,CHAT,1-50,Neuron enzyme for ACh,,CHAT +44,GRIN2A,1-50,NMDA receptors,,GRIN2A +45,GABRD,1-50,GABA receptors,,GABRD +46,GABRA1,1-50,GABA receptors,,GABRA1 +47,TREM2,1-50,microglial marker,,TREM2 +48,CSF1R,1-50,microglial marker,,CSF1R +49,IL10,1-50,Microglia function,,IL10 diff --git a/panel_design/split/2_top100.csv b/panel_design/split/2_top100.csv new file mode 100644 index 0000000000000000000000000000000000000000..5c0c349d929eb7c49319a89dba004718df5f1364 --- /dev/null +++ b/panel_design/split/2_top100.csv @@ -0,0 +1,93 @@ +Unnamed: 0,Gene symbol,Ranking,Annotation & Reasoning,Additional note,Gene Symbol +0,KCNG1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,KCNG1 +1,WLS,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,WLS +5,PDGFC,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PDGFC +6,VWC2L,top 50,"Use Persist to select the top 50, 100, and 150 genes",,VWC2L +7,SV2C,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,SV2C +8,GRM1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,GRM1 +9,ITGA8,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,ITGA8 +10,PTPRZ1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PTPRZ1 +11,NEAT1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,NEAT1 +14,ALCAM,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,ALCAM +15,NKAIN3,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,NKAIN3 +20,BRINP1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,BRINP1 +21,WIF1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,WIF1 +22,CALN1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,CALN1 +24,SYNPR,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,SYNPR +26,CARMIL1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,CARMIL1 +27,UBE2QL1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,UBE2QL1 +29,COL4A2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,COL4A2 +30,HTR1F,top 50,"Use Persist to select the top 50, 100, and 150 genes",,HTR1F +31,SPOCK1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,SPOCK1 +32,DOCK11,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,DOCK11 +33,GULP1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,GULP1 +34,SLC9A9,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SLC9A9 +36,FRMD3,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,FRMD3 +38,MGAT5B,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,MGAT5B +40,PTPRK,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PTPRK +41,SPATS2L,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,SPATS2L +42,GRM8,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,GRM8 +43,SILC1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SILC1 +44,MEIS2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,MEIS2 +45,TMEM144,top 50,"Use Persist to select the top 50, 100, and 150 genes",,TMEM144 +46,EYA4,top 50,"Use Persist to select the top 50, 100, and 150 genes",,EYA4 +51,KCNIP3,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,KCNIP3 +53,CLMP,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,CLMP +55,ANO2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,ANO2 +58,RNF220,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,RNF220 +59,MAPK4,top 50,"Use Persist to select the top 50, 100, and 150 genes",,MAPK4 +61,GRIA4,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,GRIA4 +63,SHISA8,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SHISA8 +65,SEMA3C,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SEMA3C +66,PCSK6,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PCSK6 +72,RPH3A,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,RPH3A +73,EPHA3,top 50,"Use Persist to select the top 50, 100, and 150 genes",,EPHA3 +74,SEMA5A,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SEMA5A +75,FBXL7,top 50,"Use Persist to select the top 50, 100, and 150 genes",,FBXL7 +76,PAPSS2,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PAPSS2 +77,UNC5B,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,UNC5B +81,CTXND1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,CTXND1 +82,KCNIP1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,KCNIP1 +83,RNF152,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,RNF152 +84,SLC24A4,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,SLC24A4 +85,CBLN4,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,CBLN4 +86,HTR2C,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,HTR2C +87,CDH20,top 50,"Use Persist to select the top 50, 100, and 150 genes",,CDH20 +90,ATP1B2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,ATP1B2 +91,LHFPL3,top 50,"Use Persist to select the top 50, 100, and 150 genes",,LHFPL3 +93,PELI2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,PELI2 +95,GREM2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,GREM2 +96,GUCY1A1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,GUCY1A1 +97,SPHKAP,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SPHKAP +98,NWD2,top 50,"Use Persist to select the top 50, 100, and 150 genes",,NWD2 +100,DENND3,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,DENND3 +101,ARAP2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,ARAP2 +102,LYPD6B,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,LYPD6B +104,PDE7B,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,PDE7B +105,MARCHF3,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,MARCHF3 +107,RBM20,top 50,"Use Persist to select the top 50, 100, and 150 genes",,RBM20 +108,ZNF385D-AS2,top 50,"Use Persist to select the top 50, 100, and 150 genes",,ZNF385D-AS2 +109,KIRREL3,top 50,"Use Persist to select the top 50, 100, and 150 genes",,KIRREL3 +110,UTRN,top 50,"Use Persist to select the top 50, 100, and 150 genes",,UTRN +112,VCAN,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,VCAN +115,KMO,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,KMO +117,GNG12-AS1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,GNG12-AS1 +121,TAFA4,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,TAFA4 +123,CRH,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,CRH +125,DCHS2,top 50,"Use Persist to select the top 50, 100, and 150 genes",,DCHS2 +126,PTHLH,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PTHLH +127,GYG2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,GYG2 +128,KCNK2,top 50,"Use Persist to select the top 50, 100, and 150 genes",,KCNK2 +130,IL1RAP,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,IL1RAP +133,SULF1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SULF1 +134,TRIB2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,TRIB2 +135,COL6A1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,COL6A1 +136,DOCK10,top 50,"Use Persist to select the top 50, 100, and 150 genes",,DOCK10 +137,LHX2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,LHX2 +138,NXPH1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,NXPH1 +139,SOX6,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,SOX6 +141,SFMBT2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,SFMBT2 +142,MBP,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,MBP +144,PDZRN4,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PDZRN4 +147,CSGALNACT1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,CSGALNACT1 +149,GRIN3A,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,GRIN3A diff --git a/panel_design/split/2_top150.csv b/panel_design/split/2_top150.csv new file mode 100644 index 0000000000000000000000000000000000000000..1a88c7f7e1f3f364f70f18de7494553ac10ac338 --- /dev/null +++ b/panel_design/split/2_top150.csv @@ -0,0 +1,151 @@ +Unnamed: 0,Gene symbol,Ranking,Annotation & Reasoning,Additional note,Gene Symbol +0,KCNG1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,KCNG1 +1,WLS,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,WLS +2,PRKCG,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,PRKCG +3,KCNG2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,KCNG2 +4,IL1RAPL2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,IL1RAPL2 +5,PDGFC,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PDGFC +6,VWC2L,top 50,"Use Persist to select the top 50, 100, and 150 genes",,VWC2L +7,SV2C,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,SV2C +8,GRM1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,GRM1 +9,ITGA8,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,ITGA8 +10,PTPRZ1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PTPRZ1 +11,NEAT1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,NEAT1 +12,FSTL4,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,FSTL4 +13,RTN4RL1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,RTN4RL1 +14,ALCAM,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,ALCAM +15,NKAIN3,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,NKAIN3 +16,SLC6A11,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,SLC6A11 +17,SHISA9,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,SHISA9 +18,IGSF21,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,IGSF21 +19,UBASH3B,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,UBASH3B +20,BRINP1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,BRINP1 +21,WIF1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,WIF1 +22,CALN1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,CALN1 +23,ERICH2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,ERICH2 +24,SYNPR,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,SYNPR +25,L3MBTL4,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,L3MBTL4 +26,CARMIL1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,CARMIL1 +27,UBE2QL1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,UBE2QL1 +28,SLC26A4,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,SLC26A4 +29,COL4A2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,COL4A2 +30,HTR1F,top 50,"Use Persist to select the top 50, 100, and 150 genes",,HTR1F +31,SPOCK1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,SPOCK1 +32,DOCK11,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,DOCK11 +33,GULP1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,GULP1 +34,SLC9A9,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SLC9A9 +35,IRS2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,IRS2 +36,FRMD3,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,FRMD3 +37,ST8SIA2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,ST8SIA2 +38,MGAT5B,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,MGAT5B +39,IRAK3,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,IRAK3 +40,PTPRK,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PTPRK +41,SPATS2L,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,SPATS2L +42,GRM8,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,GRM8 +43,SILC1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SILC1 +44,MEIS2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,MEIS2 +45,TMEM144,top 50,"Use Persist to select the top 50, 100, and 150 genes",,TMEM144 +46,EYA4,top 50,"Use Persist to select the top 50, 100, and 150 genes",,EYA4 +47,SLC2A1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,SLC2A1 +48,RGMA,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,RGMA +49,KCNH5,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,KCNH5 +50,CNTNAP3P2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,CNTNAP3P2 +51,KCNIP3,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,KCNIP3 +52,NPNT,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,NPNT +53,CLMP,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,CLMP +54,PPFIBP1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,PPFIBP1 +55,ANO2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,ANO2 +56,ASIC4,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,ASIC4 +57,NXPH2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,NXPH2 +58,RNF220,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,RNF220 +59,MAPK4,top 50,"Use Persist to select the top 50, 100, and 150 genes",,MAPK4 +60,TRPC6,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,TRPC6 +61,GRIA4,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,GRIA4 +62,ZBBX,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,ZBBX +63,SHISA8,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SHISA8 +64,CRHBP,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,CRHBP +65,SEMA3C,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SEMA3C +66,PCSK6,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PCSK6 +67,CACNA2D1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,CACNA2D1 +68,GNG4,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,GNG4 +69,ID2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,ID2 +70,DPP10-AS3,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,DPP10-AS3 +71,FRAS1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,FRAS1 +72,RPH3A,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,RPH3A +73,EPHA3,top 50,"Use Persist to select the top 50, 100, and 150 genes",,EPHA3 +74,SEMA5A,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SEMA5A +75,FBXL7,top 50,"Use Persist to select the top 50, 100, and 150 genes",,FBXL7 +76,PAPSS2,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PAPSS2 +77,UNC5B,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,UNC5B +78,ANGPT1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,ANGPT1 +79,PRKD1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,PRKD1 +80,FRMD4B,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,FRMD4B +81,CTXND1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,CTXND1 +82,KCNIP1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,KCNIP1 +83,RNF152,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,RNF152 +84,SLC24A4,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,SLC24A4 +85,CBLN4,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,CBLN4 +86,HTR2C,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,HTR2C +87,CDH20,top 50,"Use Persist to select the top 50, 100, and 150 genes",,CDH20 +88,DYSF,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,DYSF +89,RASSF5,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,RASSF5 +90,ATP1B2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,ATP1B2 +91,LHFPL3,top 50,"Use Persist to select the top 50, 100, and 150 genes",,LHFPL3 +92,NTNG1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,NTNG1 +93,PELI2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,PELI2 +94,EEF1DP3,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,EEF1DP3 +95,GREM2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,GREM2 +96,GUCY1A1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,GUCY1A1 +97,SPHKAP,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SPHKAP +98,NWD2,top 50,"Use Persist to select the top 50, 100, and 150 genes",,NWD2 +99,C12orf42,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,C12orf42 +100,DENND3,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,DENND3 +101,ARAP2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,ARAP2 +102,LYPD6B,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,LYPD6B +103,FNBP1L,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,FNBP1L +104,PDE7B,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,PDE7B +105,MARCHF3,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,MARCHF3 +106,SIPA1L2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,SIPA1L2 +107,RBM20,top 50,"Use Persist to select the top 50, 100, and 150 genes",,RBM20 +108,ZNF385D-AS2,top 50,"Use Persist to select the top 50, 100, and 150 genes",,ZNF385D-AS2 +109,KIRREL3,top 50,"Use Persist to select the top 50, 100, and 150 genes",,KIRREL3 +110,UTRN,top 50,"Use Persist to select the top 50, 100, and 150 genes",,UTRN +111,TOX,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,TOX +112,VCAN,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,VCAN +113,UST,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,UST +114,ZNF462,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,ZNF462 +115,KMO,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,KMO +116,PDZRN3,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,PDZRN3 +117,GNG12-AS1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,GNG12-AS1 +118,LDLRAD3,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,LDLRAD3 +119,TP53I11,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,TP53I11 +120,SLC6A16,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,SLC6A16 +121,TAFA4,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,TAFA4 +122,TRHDE-AS1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,TRHDE-AS1 +123,CRH,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,CRH +124,RYR3,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,RYR3 +125,DCHS2,top 50,"Use Persist to select the top 50, 100, and 150 genes",,DCHS2 +126,PTHLH,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PTHLH +127,GYG2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,GYG2 +128,KCNK2,top 50,"Use Persist to select the top 50, 100, and 150 genes",,KCNK2 +129,HS3ST2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,HS3ST2 +130,IL1RAP,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,IL1RAP +131,TMEM132C,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,TMEM132C +132,SRGAP1,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,SRGAP1 +133,SULF1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SULF1 +134,TRIB2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,TRIB2 +135,COL6A1,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,COL6A1 +136,DOCK10,top 50,"Use Persist to select the top 50, 100, and 150 genes",,DOCK10 +137,LHX2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,LHX2 +138,NXPH1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,NXPH1 +139,SOX6,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,SOX6 +140,PRELID2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,PRELID2 +141,SFMBT2,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,SFMBT2 +142,MBP,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,MBP +143,CDH9,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,CDH9 +144,PDZRN4,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PDZRN4 +145,DKK2,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,DKK2 +146,POSTN,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,POSTN +147,CSGALNACT1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,CSGALNACT1 +148,SEMA6D,top 100-150,"Use Persist to select the top 50, 100, and 150 genes",,SEMA6D +149,GRIN3A,top 50-100,"Use Persist to select the top 50, 100, and 150 genes",,GRIN3A diff --git a/panel_design/split/2_top50.csv b/panel_design/split/2_top50.csv new file mode 100644 index 0000000000000000000000000000000000000000..58b005c3af256eeba374a8c1c787c13a0817a033 --- /dev/null +++ b/panel_design/split/2_top50.csv @@ -0,0 +1,38 @@ +Unnamed: 0,Gene symbol,Ranking,Annotation & Reasoning,Additional note,Gene Symbol +0,KCNG1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,KCNG1 +5,PDGFC,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PDGFC +6,VWC2L,top 50,"Use Persist to select the top 50, 100, and 150 genes",,VWC2L +8,GRM1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,GRM1 +10,PTPRZ1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PTPRZ1 +30,HTR1F,top 50,"Use Persist to select the top 50, 100, and 150 genes",,HTR1F +33,GULP1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,GULP1 +34,SLC9A9,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SLC9A9 +40,PTPRK,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PTPRK +43,SILC1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SILC1 +45,TMEM144,top 50,"Use Persist to select the top 50, 100, and 150 genes",,TMEM144 +46,EYA4,top 50,"Use Persist to select the top 50, 100, and 150 genes",,EYA4 +59,MAPK4,top 50,"Use Persist to select the top 50, 100, and 150 genes",,MAPK4 +63,SHISA8,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SHISA8 +65,SEMA3C,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SEMA3C +66,PCSK6,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PCSK6 +73,EPHA3,top 50,"Use Persist to select the top 50, 100, and 150 genes",,EPHA3 +74,SEMA5A,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SEMA5A +75,FBXL7,top 50,"Use Persist to select the top 50, 100, and 150 genes",,FBXL7 +76,PAPSS2,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PAPSS2 +82,KCNIP1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,KCNIP1 +87,CDH20,top 50,"Use Persist to select the top 50, 100, and 150 genes",,CDH20 +91,LHFPL3,top 50,"Use Persist to select the top 50, 100, and 150 genes",,LHFPL3 +97,SPHKAP,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SPHKAP +98,NWD2,top 50,"Use Persist to select the top 50, 100, and 150 genes",,NWD2 +107,RBM20,top 50,"Use Persist to select the top 50, 100, and 150 genes",,RBM20 +108,ZNF385D-AS2,top 50,"Use Persist to select the top 50, 100, and 150 genes",,ZNF385D-AS2 +109,KIRREL3,top 50,"Use Persist to select the top 50, 100, and 150 genes",,KIRREL3 +110,UTRN,top 50,"Use Persist to select the top 50, 100, and 150 genes",,UTRN +125,DCHS2,top 50,"Use Persist to select the top 50, 100, and 150 genes",,DCHS2 +126,PTHLH,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PTHLH +128,KCNK2,top 50,"Use Persist to select the top 50, 100, and 150 genes",,KCNK2 +133,SULF1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,SULF1 +136,DOCK10,top 50,"Use Persist to select the top 50, 100, and 150 genes",,DOCK10 +138,NXPH1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,NXPH1 +144,PDZRN4,top 50,"Use Persist to select the top 50, 100, and 150 genes",,PDZRN4 +147,CSGALNACT1,top 50,"Use Persist to select the top 50, 100, and 150 genes",,CSGALNACT1 diff --git a/panel_design/split/3_top100.csv b/panel_design/split/3_top100.csv new file mode 100644 index 0000000000000000000000000000000000000000..e31dc6a9990ee6d1b82910ad5b45ab25658c3b67 --- /dev/null +++ b/panel_design/split/3_top100.csv @@ -0,0 +1,101 @@ +Unnamed: 0,Gene symbol,Ranking,Annotation & reasoning,Additional note,Data Source,Gene Symbol +0,TCL1A,1-50,B cell,,"""Identification and multimodal characterization of a specialized epithelial cell type associated with Crohn’s disease"", CD samples collected from terminal ileum (TI) and ascending colon (AC) through endoscopy and surgical",TCL1A +1,MS4A1,1-50,B cell,,,MS4A1 +2,CD79A,1-50,B cell,,,CD79A +3,BLK,50-100,B cell,,,BLK +4,FCRL1,50-100,B cell,,,FCRL1 +5,PAX5,50-100,B cell,,,PAX5 +6,TNFRSF13C,50-100,B cell,,,TNFRSF13C +7,CNR2,50-100,B cell,,,CNR2 +8,CD22,50-100,B cell,Mast,,CD22 +11,BEST4,1-50,BEST4,Canonical marker,,BEST4 +12,CA7,1-50,BEST4,Canonical marker,,CA7 +13,OTOP2,1-50,BEST4,Canonical marker,,OTOP2 +14,CA4,50-100,BEST4,Co-exp with CA7,,CA4 +15,NBPF19,50-100,BEST4,,,NBPF19 +16,NBPF14,50-100,BEST4,,,NBPF14 +20,AQP8,1-50,Clonocyte,,,AQP8 +21,CEACAM1,1-50,Clonocyte,,,CEACAM1 +22,AQP8,1-50,Colonocyte,,,AQP8 +23,CA2,50-100,Colonocyte,Multiple cell types,,CA2 +24,CA1,50-100,Colonocyte,,,CA1 +25,HMGCS2,50-100,Colonocyte,Multiple cell types,,HMGCS2 +26,CD24,50-100,Colonocyte,Multiple cell types,,CD24 +31,TOP2A,1-50,Cycling,,,TOP2A +32,MKI67,1-50,Cycling,,,MKI67 +33,HMGB2,50-100,Cycling,,,HMGB2 +34,OLFM4,1-50,Cycling/Stem,,,OLFM4 +35,CENPF,50-100,Cycling/Stem,,,CENPF +36,PRC1,50-100,Cycling/Stem,,,PRC1 +37,CCNB2,50-100,Cycling/Stem,,,CCNB2 +41,CHGB,1-50,EEC,,,CHGB +42,CHGA,1-50,EEC,,,CHGA +43,PCSK1N,50-100,EEC,,,PCSK1N +44,KCNB2,50-100,EEC,,,KCNB2 +45,NEUROD1,50-100,EEC,,,NEUROD1 +49,ACKR1,1-50,Endo,Vein,,ACKR1 +50,VWF,1-50,Endo,Cannonical endo marker,,VWF +51,PECAM1,1-50,Endo,Canonical marker,,PECAM1 +52,CLDN5,50-100,Endo,,,CLDN5 +53,SOX18,50-100,Endo,,,SOX18 +54,RAMP3,50-100,Endo,,,RAMP3 +55,RAMP2,50-100,Endo,,,RAMP2 +58,APOB,1-50,Enterocyte,Multiple cell types,,APOB +59,APOA4,1-50,Enterocyte,Multiple cell types,,APOA4 +60,APOA1,1-50,Enterocyte,,,APOA1 +61,SLC15A1,50-100,Enterocyte,,,SLC15A1 +62,SLC6A19,50-100,Enterocyte,,,SLC6A19 +68,FABP1,1-50,Epi,Multiple cell types,,FABP1 +69,COL1A2,1-50,Fibro,"Canonical marker, high expression",,COL1A2 +70,COL1A1,1-50,Fibro,"Canonical marker, high expression",,COL1A1 +71,DCN,1-50,Fibro,"Canonical marker, high expression",,DCN +72,COL3A1,50-100,Fibro,,,COL3A1 +73,PDGFRA,50-100,Fibro,,,PDGFRA +74,MFAP4,50-100,Fibro,,,MFAP4 +75,SFRP2,50-100,Fibro,,,SFRP2 +77,TFF3,1-50,Goblet,"Canonical marker, high expression",,TFF3 +78,MUC2,1-50,Goblet,"Canonical marker, high expression",,MUC2 +79,SPINK4,1-50,Goblet,,,SPINK4 +80,ITLN1,50-100,Goblet,,,ITLN1 +81,CLCA1,50-100,Goblet,,,CLCA1 +82,FCGBP,50-100,Goblet,,,FCGBP +84,DUOX2,1-50,LND,Important cell state in disease,,DUOX2 +85,LCN2,1-50,LND,Important cell state in disease,,LCN2 +86,DMBT1,1-50,LND,Important cell state in disease,,DMBT1 +87,REG1A,1-50,LND,Important cell state in disease,,REG1A +88,SAA1,50-100,LND,,,SAA1 +89,NOS2,50-100,LND,,,NOS2 +93,CPA3,1-50,Mast,,,CPA3 +94,KIT,1-50,Mast,,,KIT +95,CTSG,50-100,Mast,,,CTSG +96,GATA2,50-100,Mast,,,GATA2 +97,TPSAB1,50-100,Mast,,,TPSAB1 +98,TPSB2,50-100,Mast,,,TPSB2 +101,C1QA,1-50,Myel,"Canonical myeloid marker, too high expression",,C1QA +102,C1QB,1-50,Myel,Canonical myeloid marker,,C1QB +103,C1QC,50-100,Myel,Canonical myeloid marker; co-express with C1QA and C1QB,,C1QC +104,CSF3R,50-100,Myel,,,CSF3R +111,S100A8,1-50,Neutrophils,,,S100A8 +112,S100A9,1-50,Neutrophils,,,S100A9 +113,NKG7,1-50,NK,,,NKG7 +114,DEFA6,1-50,Paneth,,,DEFA6 +115,DEFA5,50-100,Paneth,,,DEFA5 +119,IGHA1,1-50,PCs,,,IGHA1 +120,JCHAIN,1-50,PCs,,,JCHAIN +121,IGHA2,1-50,PCs,,,IGHA2 +122,IGKC,50-100,PCs,"Canonical marker, multiple cell types, too high expression",,IGKC +123,CCR10,50-100,PCs,,,CCR10 +124,MZB1,50-100,PCs,,,MZB1 +129,LGR5,1-50,Stem,,,LGR5 +130,CD3D,1-50,T,,,CD3D +131,CD8A,1-50,T,,,CD8A +132,TRAC,1-50,T,,,TRAC +133,FOXP3,1-50,T,Tregs,,FOXP3 +134,CTLA4,1-50,T,,,CTLA4 +135,GZMB,1-50,T,T-cyto,,GZMB +136,CD4,50-100,T,,,CD4 +137,CCL5,50-100,T,,,CCL5 +138,CD3E,50-100,T,,,CD3E +143,LRMP,1-50,Tuft,,,LRMP +144,POU2F3,50-100,Tuft,,,POU2F3 +145,HPGDS,50-100,Tuft,,,HPGDS diff --git a/panel_design/split/3_top150.csv b/panel_design/split/3_top150.csv new file mode 100644 index 0000000000000000000000000000000000000000..14da20d57b37d7b6945039b29d01142c7ea77496 --- /dev/null +++ b/panel_design/split/3_top150.csv @@ -0,0 +1,151 @@ +Unnamed: 0,Gene symbol,Ranking,Annotation & reasoning,Additional note,Data Source,Gene Symbol +0,TCL1A,1-50,B cell,,"""Identification and multimodal characterization of a specialized epithelial cell type associated with Crohn’s disease"", CD samples collected from terminal ileum (TI) and ascending colon (AC) through endoscopy and surgical",TCL1A +1,MS4A1,1-50,B cell,,,MS4A1 +2,CD79A,1-50,B cell,,,CD79A +3,BLK,50-100,B cell,,,BLK +4,FCRL1,50-100,B cell,,,FCRL1 +5,PAX5,50-100,B cell,,,PAX5 +6,TNFRSF13C,50-100,B cell,,,TNFRSF13C +7,CNR2,50-100,B cell,,,CNR2 +8,CD22,50-100,B cell,Mast,,CD22 +9,FAM129C,100-150,B cell,,,FAM129C +10,VPREB3,100-150,B cell,,,VPREB3 +11,BEST4,1-50,BEST4,Canonical marker,,BEST4 +12,CA7,1-50,BEST4,Canonical marker,,CA7 +13,OTOP2,1-50,BEST4,Canonical marker,,OTOP2 +14,CA4,50-100,BEST4,Co-exp with CA7,,CA4 +15,NBPF19,50-100,BEST4,,,NBPF19 +16,NBPF14,50-100,BEST4,,,NBPF14 +17,MEIS1,100-150,BEST4,,,MEIS1 +18,ADCY5,100-150,BEST4,,,ADCY5 +19,SPIB,100-150,BEST4,,,SPIB +20,AQP8,1-50,Clonocyte,,,AQP8 +21,CEACAM1,1-50,Clonocyte,,,CEACAM1 +22,AQP8,1-50,Colonocyte,,,AQP8 +23,CA2,50-100,Colonocyte,Multiple cell types,,CA2 +24,CA1,50-100,Colonocyte,,,CA1 +25,HMGCS2,50-100,Colonocyte,Multiple cell types,,HMGCS2 +26,CD24,50-100,Colonocyte,Multiple cell types,,CD24 +27,MS4A12,100-150,Colonocyte,,,MS4A12 +28,SLC37A2,100-150,Colonocyte,,,SLC37A2 +29,CEACAM7,100-150,Colonocyte,,,CEACAM7 +30,SLC26A2,100-150,Colonocyte,,,SLC26A2 +31,TOP2A,1-50,Cycling,,,TOP2A +32,MKI67,1-50,Cycling,,,MKI67 +33,HMGB2,50-100,Cycling,,,HMGB2 +34,OLFM4,1-50,Cycling/Stem,,,OLFM4 +35,CENPF,50-100,Cycling/Stem,,,CENPF +36,PRC1,50-100,Cycling/Stem,,,PRC1 +37,CCNB2,50-100,Cycling/Stem,,,CCNB2 +38,AURKB,100-150,Cycling/Stem,,,AURKB +39,GTSE1,100-150,Cycling/Stem,,,GTSE1 +40,RRM2,100-150,Cycling/Stem,,,RRM2 +41,CHGB,1-50,EEC,,,CHGB +42,CHGA,1-50,EEC,,,CHGA +43,PCSK1N,50-100,EEC,,,PCSK1N +44,KCNB2,50-100,EEC,,,KCNB2 +45,NEUROD1,50-100,EEC,,,NEUROD1 +46,FEV,100-150,EEC,,,FEV +47,SCG2,100-150,EEC,,,SCG2 +48,SSTR5-AS1,100-150,EEC,,,SSTR5-AS1 +49,ACKR1,1-50,Endo,Vein,,ACKR1 +50,VWF,1-50,Endo,Cannonical endo marker,,VWF +51,PECAM1,1-50,Endo,Canonical marker,,PECAM1 +52,CLDN5,50-100,Endo,,,CLDN5 +53,SOX18,50-100,Endo,,,SOX18 +54,RAMP3,50-100,Endo,,,RAMP3 +55,RAMP2,50-100,Endo,,,RAMP2 +56,CLEC14A,100-150,Endo,,,CLEC14A +57,TIE1,100-150,Endo,,,TIE1 +58,APOB,1-50,Enterocyte,Multiple cell types,,APOB +59,APOA4,1-50,Enterocyte,Multiple cell types,,APOA4 +60,APOA1,1-50,Enterocyte,,,APOA1 +61,SLC15A1,50-100,Enterocyte,,,SLC15A1 +62,SLC6A19,50-100,Enterocyte,,,SLC6A19 +63,CYP3A4,100-150,Enterocyte,,,CYP3A4 +64,MTTP,100-150,Enterocyte,,,MTTP +65,CUBN,100-150,Enterocyte,,,CUBN +66,SLC10A2,100-150,Enterocyte,,,SLC10A2 +67,SLC7A9,100-150,Enterocyte,,,SLC7A9 +68,FABP1,1-50,Epi,Multiple cell types,,FABP1 +69,COL1A2,1-50,Fibro,"Canonical marker, high expression",,COL1A2 +70,COL1A1,1-50,Fibro,"Canonical marker, high expression",,COL1A1 +71,DCN,1-50,Fibro,"Canonical marker, high expression",,DCN +72,COL3A1,50-100,Fibro,,,COL3A1 +73,PDGFRA,50-100,Fibro,,,PDGFRA +74,MFAP4,50-100,Fibro,,,MFAP4 +75,SFRP2,50-100,Fibro,,,SFRP2 +76,C1R,100-150,Fibro,,,C1R +77,TFF3,1-50,Goblet,"Canonical marker, high expression",,TFF3 +78,MUC2,1-50,Goblet,"Canonical marker, high expression",,MUC2 +79,SPINK4,1-50,Goblet,,,SPINK4 +80,ITLN1,50-100,Goblet,,,ITLN1 +81,CLCA1,50-100,Goblet,,,CLCA1 +82,FCGBP,50-100,Goblet,,,FCGBP +83,BEST2,100-150,Goblet,,,BEST2 +84,DUOX2,1-50,LND,Important cell state in disease,,DUOX2 +85,LCN2,1-50,LND,Important cell state in disease,,LCN2 +86,DMBT1,1-50,LND,Important cell state in disease,,DMBT1 +87,REG1A,1-50,LND,Important cell state in disease,,REG1A +88,SAA1,50-100,LND,,,SAA1 +89,NOS2,50-100,LND,,,NOS2 +90,PI3,100-150,LND,,,PI3 +91,PDZK1IP1,100-150,LND,,,PDZK1IP1 +92,CD55,100-150,LND,,,CD55 +93,CPA3,1-50,Mast,,,CPA3 +94,KIT,1-50,Mast,,,KIT +95,CTSG,50-100,Mast,,,CTSG +96,GATA2,50-100,Mast,,,GATA2 +97,TPSAB1,50-100,Mast,,,TPSAB1 +98,TPSB2,50-100,Mast,,,TPSB2 +99,MS4A2,100-150,Mast,,,MS4A2 +100,HDC,100-150,Mast,,,HDC +101,C1QA,1-50,Myel,"Canonical myeloid marker, too high expression",,C1QA +102,C1QB,1-50,Myel,Canonical myeloid marker,,C1QB +103,C1QC,50-100,Myel,Canonical myeloid marker; co-express with C1QA and C1QB,,C1QC +104,CSF3R,50-100,Myel,,,CSF3R +105,FPR1,100-150,Myel,,,FPR1 +106,MS4A6A,100-150,Myel,,,MS4A6A +107,TYROBP,100-150,Myel,,,TYROBP +108,AIF1,100-150,Myel,,,AIF1 +109,MS4A7,100-150,Myel,,,MS4A7 +110,CSF2RA,100-150,Myel,,,CSF2RA +111,S100A8,1-50,Neutrophils,,,S100A8 +112,S100A9,1-50,Neutrophils,,,S100A9 +113,NKG7,1-50,NK,,,NKG7 +114,DEFA6,1-50,Paneth,,,DEFA6 +115,DEFA5,50-100,Paneth,,,DEFA5 +116,ITLN2,100-150,Paneth,,,ITLN2 +117,PLA2G2A,100-150,Paneth,,,PLA2G2A +118,CDKN1C,100-150,Paneth,,,CDKN1C +119,IGHA1,1-50,PCs,,,IGHA1 +120,JCHAIN,1-50,PCs,,,JCHAIN +121,IGHA2,1-50,PCs,,,IGHA2 +122,IGKC,50-100,PCs,"Canonical marker, multiple cell types, too high expression",,IGKC +123,CCR10,50-100,PCs,,,CCR10 +124,MZB1,50-100,PCs,,,MZB1 +125,DERL3,100-150,PCs,,,DERL3 +126,TNFRSF17,100-150,PCs,,,TNFRSF17 +127,AC096579.15,100-150,PCs,,,AC096579.15 +128,ENAM,100-150,PCs,,,ENAM +129,LGR5,1-50,Stem,,,LGR5 +130,CD3D,1-50,T,,,CD3D +131,CD8A,1-50,T,,,CD8A +132,TRAC,1-50,T,,,TRAC +133,FOXP3,1-50,T,Tregs,,FOXP3 +134,CTLA4,1-50,T,,,CTLA4 +135,GZMB,1-50,T,T-cyto,,GZMB +136,CD4,50-100,T,,,CD4 +137,CCL5,50-100,T,,,CCL5 +138,CD3E,50-100,T,,,CD3E +139,CD247,100-150,T,,,CD247 +140,TRBC1,100-150,T,,,TRBC1 +141,AC092580.4,100-150,T,,,AC092580.4 +142,CD96,100-150,T,,,CD96 +143,LRMP,1-50,Tuft,,,LRMP +144,POU2F3,50-100,Tuft,,,POU2F3 +145,HPGDS,50-100,Tuft,,,HPGDS +146,SH2D6,100-150,Tuft,,,SH2D6 +147,CCDC129,100-150,Tuft,,,CCDC129 +148,SH2D7,100-150,Tuft,,,SH2D7 +149,PTGS1,100-150,Tuft,,,PTGS1 diff --git a/panel_design/split/3_top50.csv b/panel_design/split/3_top50.csv new file mode 100644 index 0000000000000000000000000000000000000000..9c742437c7485334c85d190cae0b9a673cea875a --- /dev/null +++ b/panel_design/split/3_top50.csv @@ -0,0 +1,51 @@ +Unnamed: 0,Gene symbol,Ranking,Annotation & reasoning,Additional note,Data Source,Gene Symbol +0,TCL1A,1-50,B cell,,"""Identification and multimodal characterization of a specialized epithelial cell type associated with Crohn’s disease"", CD samples collected from terminal ileum (TI) and ascending colon (AC) through endoscopy and surgical",TCL1A +1,MS4A1,1-50,B cell,,,MS4A1 +2,CD79A,1-50,B cell,,,CD79A +11,BEST4,1-50,BEST4,Canonical marker,,BEST4 +12,CA7,1-50,BEST4,Canonical marker,,CA7 +13,OTOP2,1-50,BEST4,Canonical marker,,OTOP2 +20,AQP8,1-50,Clonocyte,,,AQP8 +21,CEACAM1,1-50,Clonocyte,,,CEACAM1 +22,AQP8,1-50,Colonocyte,,,AQP8 +31,TOP2A,1-50,Cycling,,,TOP2A +32,MKI67,1-50,Cycling,,,MKI67 +34,OLFM4,1-50,Cycling/Stem,,,OLFM4 +41,CHGB,1-50,EEC,,,CHGB +42,CHGA,1-50,EEC,,,CHGA +49,ACKR1,1-50,Endo,Vein,,ACKR1 +50,VWF,1-50,Endo,Cannonical endo marker,,VWF +51,PECAM1,1-50,Endo,Canonical marker,,PECAM1 +58,APOB,1-50,Enterocyte,Multiple cell types,,APOB +59,APOA4,1-50,Enterocyte,Multiple cell types,,APOA4 +60,APOA1,1-50,Enterocyte,,,APOA1 +68,FABP1,1-50,Epi,Multiple cell types,,FABP1 +69,COL1A2,1-50,Fibro,"Canonical marker, high expression",,COL1A2 +70,COL1A1,1-50,Fibro,"Canonical marker, high expression",,COL1A1 +71,DCN,1-50,Fibro,"Canonical marker, high expression",,DCN +77,TFF3,1-50,Goblet,"Canonical marker, high expression",,TFF3 +78,MUC2,1-50,Goblet,"Canonical marker, high expression",,MUC2 +79,SPINK4,1-50,Goblet,,,SPINK4 +84,DUOX2,1-50,LND,Important cell state in disease,,DUOX2 +85,LCN2,1-50,LND,Important cell state in disease,,LCN2 +86,DMBT1,1-50,LND,Important cell state in disease,,DMBT1 +87,REG1A,1-50,LND,Important cell state in disease,,REG1A +93,CPA3,1-50,Mast,,,CPA3 +94,KIT,1-50,Mast,,,KIT +101,C1QA,1-50,Myel,"Canonical myeloid marker, too high expression",,C1QA +102,C1QB,1-50,Myel,Canonical myeloid marker,,C1QB +111,S100A8,1-50,Neutrophils,,,S100A8 +112,S100A9,1-50,Neutrophils,,,S100A9 +113,NKG7,1-50,NK,,,NKG7 +114,DEFA6,1-50,Paneth,,,DEFA6 +119,IGHA1,1-50,PCs,,,IGHA1 +120,JCHAIN,1-50,PCs,,,JCHAIN +121,IGHA2,1-50,PCs,,,IGHA2 +129,LGR5,1-50,Stem,,,LGR5 +130,CD3D,1-50,T,,,CD3D +131,CD8A,1-50,T,,,CD8A +132,TRAC,1-50,T,,,TRAC +133,FOXP3,1-50,T,Tregs,,FOXP3 +134,CTLA4,1-50,T,,,CTLA4 +135,GZMB,1-50,T,T-cyto,,GZMB +143,LRMP,1-50,Tuft,,,LRMP diff --git a/panel_design/split/4_top100.csv b/panel_design/split/4_top100.csv new file mode 100644 index 0000000000000000000000000000000000000000..3362df9415d79d5c885b9f9edf3006f6100f56de --- /dev/null +++ b/panel_design/split/4_top100.csv @@ -0,0 +1,103 @@ +Unnamed: 0.1,Unnamed: 0,Gene.Symbol,Ranking,Annotation...reasoning,Additional.note,Gene Symbol +0,1,FSTL4,1-50,More distinct marker than L5,markers ranked with cohen mean,FSTL4 +1,2,SATB2,1-50,abundant marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean,SATB2 +2,3,KCNIP4,1-50,abundant marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean,KCNIP4 +3,4,TAFA1,1-50,abundant marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean,TAFA1 +4,5,VAT1L,1-50,abundant marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean,VAT1L +5,6,CBLN2,1-50,abundant marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean,CBLN2 +6,7,ARPP21,1-50,abundant marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean,ARPP21 +7,8,RAD52,1-50,abundant marker for astrocyte of the cerebral cortex,markers ranked with cohen mean,RAD52 +8,9,PDK4,1-50,abundant marker for astrocyte of the cerebral cortex,markers ranked with cohen mean,PDK4 +9,10,SEMA3B,1-50,abundant marker for astrocyte of the cerebral cortex,markers ranked with cohen mean,SEMA3B +10,11,ADARB2,1-50,abundant marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,ADARB2 +11,12,SORCS3,1-50,abundant marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,SORCS3 +12,13,CXCL14,1-50,abundant marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,CXCL14 +13,14,MAD1L1,1-50,abundant marker for cerebral cortex endothelial cell,markers ranked with cohen mean,MAD1L1 +14,15,CYP26B1,1-50,abundant marker for cerebral cortex endothelial cell,markers ranked with cohen mean,CYP26B1 +15,16,CASP10,1-50,abundant marker for cerebral cortex endothelial cell,markers ranked with cohen mean,CASP10 +16,17,ZNF536,1-50,abundant marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,ZNF536 +17,18,ZNF385D,1-50,abundant marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,ZNF385D +18,19,THSD7A,1-50,abundant marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,THSD7A +19,20,SEMA3E,1-50,abundant marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean,SEMA3E +20,21,EGFEM1P,1-50,abundant marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean,EGFEM1P +21,22,LAMP5,1-50,abundant marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean,LAMP5 +22,23,FGF13,1-50,abundant marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean,FGF13 +23,24,C1orf112,1-50,abundant marker for microglial cell,markers ranked with cohen mean,C1orf112 +24,25,CEACAM21,1-50,abundant marker for microglial cell,markers ranked with cohen mean,CEACAM21 +25,26,TYROBP,1-50,abundant marker for microglial cell,markers ranked with cohen mean,TYROBP +26,27,TSHZ2,1-50,abundant marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean,TSHZ2 +27,28,HTR2C,1-50,abundant marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean,HTR2C +28,29,GCFC2,1-50,abundant marker for oligodendrocyte,markers ranked with cohen mean,GCFC2 +29,30,LAMP2,1-50,abundant marker for oligodendrocyte,markers ranked with cohen mean,LAMP2 +30,31,TMEM98,1-50,abundant marker for oligodendrocyte,markers ranked with cohen mean,TMEM98 +31,32,HECW1,1-50,abundant marker for oligodendrocyte precursor cell,markers ranked with cohen mean,HECW1 +32,33,KLHL13,1-50,abundant marker for oligodendrocyte precursor cell,markers ranked with cohen mean,KLHL13 +33,34,ATP1A2,1-50,abundant marker for oligodendrocyte precursor cell,markers ranked with cohen mean,ATP1A2 +34,35,ABTB3,1-50,abundant marker for pvalb GABAergic cortical interneuron,markers ranked with cohen mean,ABTB3 +35,36,GCLC,1-50,abundant marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean,GCLC +36,37,HCCS,1-50,abundant marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean,HCCS +37,38,DPEP1,1-50,abundant marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean,DPEP1 +38,39,SST,1-50,abundant marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,SST +39,40,GRIK1,1-50,abundant marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,GRIK1 +40,41,SYNPR,1-50,abundant marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,SYNPR +41,42,ATP1A2,1-50,abundant marker for vascular leptomeningeal cell,markers ranked with cohen mean,ATP1A2 +42,43,EBF1,1-50,abundant marker for vascular leptomeningeal cell,markers ranked with cohen mean,EBF1 +43,44,PDGFRB,1-50,abundant marker for vascular leptomeningeal cell,markers ranked with cohen mean,PDGFRB +44,45,VIP,1-50,abundant marker for vip GABAergic cortical interneuron,markers ranked with cohen mean,VIP +45,46,GALNTL6,1-50,abundant marker for vip GABAergic cortical interneuron,markers ranked with cohen mean,GALNTL6 +46,47,CX3CR1,1-50,abundant marker for microglial cell,Known Marker,CX3CR1 +47,48,DLGAP2,50-100,Less specific marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean,DLGAP2 +48,49,STXBP5L,50-100,Less specific marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean,STXBP5L +49,50,CHRM3,50-100,Less specific marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean,CHRM3 +50,51,NRGN,50-100,Less specific marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean,NRGN +51,52,PDE1A,50-100,Less specific marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean,PDE1A +52,53,RALYL,50-100,Less specific marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean,RALYL +53,54,PTPRR,50-100,Less specific marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean,PTPRR +54,55,MARCHF1,50-100,Less specific marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean,MARCHF1 +55,56,NKX2-2,50-100,Less specific marker for astrocyte of the cerebral cortex,markers ranked with cohen mean,NKX2-2 +56,57,OBI1-AS1,50-100,Less specific marker for astrocyte of the cerebral cortex,markers ranked with cohen mean,OBI1-AS1 +57,58,CRACD,50-100,Less specific marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,CRACD +58,59,MYO16,50-100,Less specific marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,MYO16 +59,60,CACNA1B,50-100,Less specific marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,CACNA1B +60,61,ID3,50-100,Less specific marker for cerebral cortex endothelial cell,markers ranked with cohen mean,ID3 +61,62,TBX3,50-100,Less specific marker for cerebral cortex endothelial cell,markers ranked with cohen mean,TBX3 +62,63,PLXND1,50-100,Less specific marker for cerebral cortex endothelial cell,markers ranked with cohen mean,PLXND1 +63,64,TMEM132D,50-100,Less specific marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,TMEM132D +64,65,TENM1,50-100,Less specific marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,TENM1 +65,66,SDK1,50-100,Less specific marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,SDK1 +66,67,CLSTN2,50-100,Less specific marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean,CLSTN2 +67,68,RYR2,50-100,Less specific marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean,RYR2 +68,69,NRG1,50-100,Less specific marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean,NRG1 +69,70,NYAP2,50-100,Less specific marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean,NYAP2 +70,71,MTUS2,50-100,Less specific marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean,MTUS2 +71,72,LINC00299,50-100,Less specific marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean,LINC00299 +72,73,APBB1IP,50-100,Less specific marker for microglial cell,markers ranked with cohen mean,APBB1IP +73,74,SH3BP2,50-100,Less specific marker for microglial cell,markers ranked with cohen mean,SH3BP2 +74,75,C1QC,50-100,Less specific marker for microglial cell,markers ranked with cohen mean,C1QC +75,76,FOXP2,50-100,Less specific marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean,FOXP2 +76,77,CHN2,50-100,Less specific marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean,CHN2 +77,78,MED24,50-100,Less specific marker for oligodendrocyte,markers ranked with cohen mean,MED24 +78,79,DAPK2,50-100,Less specific marker for oligodendrocyte,markers ranked with cohen mean,DAPK2 +79,80,BCAS1,50-100,Less specific marker for oligodendrocyte,markers ranked with cohen mean,BCAS1 +80,81,CTNS,50-100,Less specific marker for oligodendrocyte precursor cell,markers ranked with cohen mean,CTNS +81,82,BCAS1,50-100,Less specific marker for oligodendrocyte precursor cell,markers ranked with cohen mean,BCAS1 +82,83,SOX6,50-100,Less specific marker for oligodendrocyte precursor cell,markers ranked with cohen mean,SOX6 +83,84,ADAMTS17,50-100,Less specific marker for pvalb GABAergic cortical interneuron,markers ranked with cohen mean,ADAMTS17 +84,85,FGF12,50-100,Less specific marker for pvalb GABAergic cortical interneuron,markers ranked with cohen mean,FGF12 +85,86,GRIP1,50-100,Less specific marker for pvalb GABAergic cortical interneuron,markers ranked with cohen mean,GRIP1 +86,87,KMO,50-100,Less specific marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean,KMO +87,88,KCNK17,50-100,Less specific marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean,KCNK17 +88,89,STXBP6,50-100,Less specific marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,STXBP6 +89,90,CDH9,50-100,Less specific marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,CDH9 +90,91,ELAVL2,50-100,Less specific marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,ELAVL2 +91,92,UTRN,50-100,Less specific marker for vascular leptomeningeal cell,markers ranked with cohen mean,UTRN +92,93,CALD1,50-100,Less specific marker for vascular leptomeningeal cell,markers ranked with cohen mean,CALD1 +93,94,LAMA2,50-100,Less specific marker for vascular leptomeningeal cell,markers ranked with cohen mean,LAMA2 +94,95,GALNT13,50-100,Less specific marker for vip GABAergic cortical interneuron,markers ranked with cohen mean,GALNT13 +95,96,SNTG1,50-100,Less specific marker for vip GABAergic cortical interneuron,markers ranked with cohen mean,SNTG1 +144,145,ALDH1L1,1-50,known marker gene for astrocyte of the cerebral cortex,sourced from prior knowledge,ALDH1L1 +145,146,MBP,50-100,known marker gene for oligodendrocyte,sourced from prior knowledge,MBP +146,147,GFAP,50-100,known marker gene for astrocyte,sourced from prior knowledge,GFAP +147,148,AQP4,1-50,known marker gene for astrocyte,sourced from prior knowledge,AQP4 +148,149,PVALB,50-100,spcific marker for pvalb interneurons,sourced from prior knowledge,PVALB +149,150,SST,1-50,known marker gene SST interneurons,sourced from prior knowledge,SST diff --git a/panel_design/split/4_top150.csv b/panel_design/split/4_top150.csv new file mode 100644 index 0000000000000000000000000000000000000000..baf65e3bafc224025a3ac10ee18e3387eeda027b --- /dev/null +++ b/panel_design/split/4_top150.csv @@ -0,0 +1,151 @@ +Unnamed: 0.1,Unnamed: 0,Gene.Symbol,Ranking,Annotation...reasoning,Additional.note,Gene Symbol +0,1,FSTL4,1-50,More distinct marker than L5,markers ranked with cohen mean,FSTL4 +1,2,SATB2,1-50,abundant marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean,SATB2 +2,3,KCNIP4,1-50,abundant marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean,KCNIP4 +3,4,TAFA1,1-50,abundant marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean,TAFA1 +4,5,VAT1L,1-50,abundant marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean,VAT1L +5,6,CBLN2,1-50,abundant marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean,CBLN2 +6,7,ARPP21,1-50,abundant marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean,ARPP21 +7,8,RAD52,1-50,abundant marker for astrocyte of the cerebral cortex,markers ranked with cohen mean,RAD52 +8,9,PDK4,1-50,abundant marker for astrocyte of the cerebral cortex,markers ranked with cohen mean,PDK4 +9,10,SEMA3B,1-50,abundant marker for astrocyte of the cerebral cortex,markers ranked with cohen mean,SEMA3B +10,11,ADARB2,1-50,abundant marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,ADARB2 +11,12,SORCS3,1-50,abundant marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,SORCS3 +12,13,CXCL14,1-50,abundant marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,CXCL14 +13,14,MAD1L1,1-50,abundant marker for cerebral cortex endothelial cell,markers ranked with cohen mean,MAD1L1 +14,15,CYP26B1,1-50,abundant marker for cerebral cortex endothelial cell,markers ranked with cohen mean,CYP26B1 +15,16,CASP10,1-50,abundant marker for cerebral cortex endothelial cell,markers ranked with cohen mean,CASP10 +16,17,ZNF536,1-50,abundant marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,ZNF536 +17,18,ZNF385D,1-50,abundant marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,ZNF385D +18,19,THSD7A,1-50,abundant marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,THSD7A +19,20,SEMA3E,1-50,abundant marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean,SEMA3E +20,21,EGFEM1P,1-50,abundant marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean,EGFEM1P +21,22,LAMP5,1-50,abundant marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean,LAMP5 +22,23,FGF13,1-50,abundant marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean,FGF13 +23,24,C1orf112,1-50,abundant marker for microglial cell,markers ranked with cohen mean,C1orf112 +24,25,CEACAM21,1-50,abundant marker for microglial cell,markers ranked with cohen mean,CEACAM21 +25,26,TYROBP,1-50,abundant marker for microglial cell,markers ranked with cohen mean,TYROBP +26,27,TSHZ2,1-50,abundant marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean,TSHZ2 +27,28,HTR2C,1-50,abundant marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean,HTR2C +28,29,GCFC2,1-50,abundant marker for oligodendrocyte,markers ranked with cohen mean,GCFC2 +29,30,LAMP2,1-50,abundant marker for oligodendrocyte,markers ranked with cohen mean,LAMP2 +30,31,TMEM98,1-50,abundant marker for oligodendrocyte,markers ranked with cohen mean,TMEM98 +31,32,HECW1,1-50,abundant marker for oligodendrocyte precursor cell,markers ranked with cohen mean,HECW1 +32,33,KLHL13,1-50,abundant marker for oligodendrocyte precursor cell,markers ranked with cohen mean,KLHL13 +33,34,ATP1A2,1-50,abundant marker for oligodendrocyte precursor cell,markers ranked with cohen mean,ATP1A2 +34,35,ABTB3,1-50,abundant marker for pvalb GABAergic cortical interneuron,markers ranked with cohen mean,ABTB3 +35,36,GCLC,1-50,abundant marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean,GCLC +36,37,HCCS,1-50,abundant marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean,HCCS +37,38,DPEP1,1-50,abundant marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean,DPEP1 +38,39,SST,1-50,abundant marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,SST +39,40,GRIK1,1-50,abundant marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,GRIK1 +40,41,SYNPR,1-50,abundant marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,SYNPR +41,42,ATP1A2,1-50,abundant marker for vascular leptomeningeal cell,markers ranked with cohen mean,ATP1A2 +42,43,EBF1,1-50,abundant marker for vascular leptomeningeal cell,markers ranked with cohen mean,EBF1 +43,44,PDGFRB,1-50,abundant marker for vascular leptomeningeal cell,markers ranked with cohen mean,PDGFRB +44,45,VIP,1-50,abundant marker for vip GABAergic cortical interneuron,markers ranked with cohen mean,VIP +45,46,GALNTL6,1-50,abundant marker for vip GABAergic cortical interneuron,markers ranked with cohen mean,GALNTL6 +46,47,CX3CR1,1-50,abundant marker for microglial cell,Known Marker,CX3CR1 +47,48,DLGAP2,50-100,Less specific marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean,DLGAP2 +48,49,STXBP5L,50-100,Less specific marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean,STXBP5L +49,50,CHRM3,50-100,Less specific marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean,CHRM3 +50,51,NRGN,50-100,Less specific marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean,NRGN +51,52,PDE1A,50-100,Less specific marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean,PDE1A +52,53,RALYL,50-100,Less specific marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean,RALYL +53,54,PTPRR,50-100,Less specific marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean,PTPRR +54,55,MARCHF1,50-100,Less specific marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean,MARCHF1 +55,56,NKX2-2,50-100,Less specific marker for astrocyte of the cerebral cortex,markers ranked with cohen mean,NKX2-2 +56,57,OBI1-AS1,50-100,Less specific marker for astrocyte of the cerebral cortex,markers ranked with cohen mean,OBI1-AS1 +57,58,CRACD,50-100,Less specific marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,CRACD +58,59,MYO16,50-100,Less specific marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,MYO16 +59,60,CACNA1B,50-100,Less specific marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,CACNA1B +60,61,ID3,50-100,Less specific marker for cerebral cortex endothelial cell,markers ranked with cohen mean,ID3 +61,62,TBX3,50-100,Less specific marker for cerebral cortex endothelial cell,markers ranked with cohen mean,TBX3 +62,63,PLXND1,50-100,Less specific marker for cerebral cortex endothelial cell,markers ranked with cohen mean,PLXND1 +63,64,TMEM132D,50-100,Less specific marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,TMEM132D +64,65,TENM1,50-100,Less specific marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,TENM1 +65,66,SDK1,50-100,Less specific marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,SDK1 +66,67,CLSTN2,50-100,Less specific marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean,CLSTN2 +67,68,RYR2,50-100,Less specific marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean,RYR2 +68,69,NRG1,50-100,Less specific marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean,NRG1 +69,70,NYAP2,50-100,Less specific marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean,NYAP2 +70,71,MTUS2,50-100,Less specific marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean,MTUS2 +71,72,LINC00299,50-100,Less specific marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean,LINC00299 +72,73,APBB1IP,50-100,Less specific marker for microglial cell,markers ranked with cohen mean,APBB1IP +73,74,SH3BP2,50-100,Less specific marker for microglial cell,markers ranked with cohen mean,SH3BP2 +74,75,C1QC,50-100,Less specific marker for microglial cell,markers ranked with cohen mean,C1QC +75,76,FOXP2,50-100,Less specific marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean,FOXP2 +76,77,CHN2,50-100,Less specific marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean,CHN2 +77,78,MED24,50-100,Less specific marker for oligodendrocyte,markers ranked with cohen mean,MED24 +78,79,DAPK2,50-100,Less specific marker for oligodendrocyte,markers ranked with cohen mean,DAPK2 +79,80,BCAS1,50-100,Less specific marker for oligodendrocyte,markers ranked with cohen mean,BCAS1 +80,81,CTNS,50-100,Less specific marker for oligodendrocyte precursor cell,markers ranked with cohen mean,CTNS +81,82,BCAS1,50-100,Less specific marker for oligodendrocyte precursor cell,markers ranked with cohen mean,BCAS1 +82,83,SOX6,50-100,Less specific marker for oligodendrocyte precursor cell,markers ranked with cohen mean,SOX6 +83,84,ADAMTS17,50-100,Less specific marker for pvalb GABAergic cortical interneuron,markers ranked with cohen mean,ADAMTS17 +84,85,FGF12,50-100,Less specific marker for pvalb GABAergic cortical interneuron,markers ranked with cohen mean,FGF12 +85,86,GRIP1,50-100,Less specific marker for pvalb GABAergic cortical interneuron,markers ranked with cohen mean,GRIP1 +86,87,KMO,50-100,Less specific marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean,KMO +87,88,KCNK17,50-100,Less specific marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean,KCNK17 +88,89,STXBP6,50-100,Less specific marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,STXBP6 +89,90,CDH9,50-100,Less specific marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,CDH9 +90,91,ELAVL2,50-100,Less specific marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,ELAVL2 +91,92,UTRN,50-100,Less specific marker for vascular leptomeningeal cell,markers ranked with cohen mean,UTRN +92,93,CALD1,50-100,Less specific marker for vascular leptomeningeal cell,markers ranked with cohen mean,CALD1 +93,94,LAMA2,50-100,Less specific marker for vascular leptomeningeal cell,markers ranked with cohen mean,LAMA2 +94,95,GALNT13,50-100,Less specific marker for vip GABAergic cortical interneuron,markers ranked with cohen mean,GALNT13 +95,96,SNTG1,50-100,Less specific marker for vip GABAergic cortical interneuron,markers ranked with cohen mean,SNTG1 +96,97,LINC01480,100-150,de-enriched marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean,LINC01480 +97,98,AIF1,100-150,de-enriched marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean,AIF1 +98,99,MGC16275,100-150,de-enriched marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean,MGC16275 +99,100,SALL3,100-150,de-enriched marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean,SALL3 +100,101,FMO6P,100-150,de-enriched marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean,FMO6P +101,102,GPRC5B,100-150,de-enriched marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean,GPRC5B +102,103,SEMA6A,100-150,de-enriched marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean,SEMA6A +103,104,CAPN2,100-150,de-enriched marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean,CAPN2 +104,105,IL1RAPL1,100-150,de-enriched marker for astrocyte of the cerebral cortex,markers ranked with cohen mean,IL1RAPL1 +105,106,DSCAM,100-150,de-enriched marker for astrocyte of the cerebral cortex,markers ranked with cohen mean,DSCAM +106,107,PPP1R13L,100-150,de-enriched marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,PPP1R13L +107,108,INPPL1,100-150,de-enriched marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,INPPL1 +108,109,EXPH5,100-150,de-enriched marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,EXPH5 +109,110,NCAM1,100-150,de-enriched marker for cerebral cortex endothelial cell,markers ranked with cohen mean,NCAM1 +110,111,GABRG3,100-150,de-enriched marker for cerebral cortex endothelial cell,markers ranked with cohen mean,GABRG3 +111,112,VRK2,100-150,de-enriched marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,VRK2 +112,113,TRPM3,100-150,de-enriched marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,TRPM3 +113,114,CSGALNACT1,100-150,de-enriched marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,CSGALNACT1 +114,115,RND3,100-150,de-enriched marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean,RND3 +115,116,NOTCH2NLA,100-150,de-enriched marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean,NOTCH2NLA +116,117,EGFR,100-150,de-enriched marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean,EGFR +117,118,DKKL1,100-150,de-enriched marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean,DKKL1 +118,119,TNFSF10,100-150,de-enriched marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean,TNFSF10 +119,120,TRIB1,100-150,de-enriched marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean,TRIB1 +120,121,DOK6,100-150,de-enriched marker for microglial cell,markers ranked with cohen mean,DOK6 +121,122,METTL6,100-150,de-enriched marker for microglial cell,markers ranked with cohen mean,METTL6 +122,123,TRIM16,100-150,de-enriched marker for microglial cell,markers ranked with cohen mean,TRIM16 +123,124,HMOX1,100-150,de-enriched marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean,HMOX1 +124,125,ETS1,100-150,de-enriched marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean,ETS1 +125,126,HERC2P4,100-150,de-enriched marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean,HERC2P4 +126,127,MYO1F,100-150,de-enriched marker for oligodendrocyte,markers ranked with cohen mean,MYO1F +127,128,GASK1B,100-150,de-enriched marker for oligodendrocyte,markers ranked with cohen mean,GASK1B +128,129,RTCB,100-150,de-enriched marker for oligodendrocyte,markers ranked with cohen mean,RTCB +129,130,RBFOX3,100-150,de-enriched marker for oligodendrocyte precursor cell,markers ranked with cohen mean,RBFOX3 +130,131,TMEM119,100-150,de-enriched marker for pvalb GABAergic cortical interneuron,markers ranked with cohen mean,TMEM119 +131,132,CAVIN2,100-150,de-enriched marker for pvalb GABAergic cortical interneuron,markers ranked with cohen mean,CAVIN2 +132,133,GBGT1,100-150,de-enriched marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean,GBGT1 +133,134,IL6ST,100-150,de-enriched marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean,IL6ST +134,135,SFMBT2,100-150,de-enriched marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean,SFMBT2 +135,136,HS3ST6,100-150,de-enriched marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,HS3ST6 +136,137,EPHA2,100-150,de-enriched marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,EPHA2 +137,138,CHST3,100-150,de-enriched marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,CHST3 +138,139,CNIH3,100-150,de-enriched marker for vascular leptomeningeal cell,markers ranked with cohen mean,CNIH3 +139,140,AGTPBP1,100-150,de-enriched marker for vascular leptomeningeal cell,markers ranked with cohen mean,AGTPBP1 +140,141,AFDN,100-150,de-enriched marker for vascular leptomeningeal cell,markers ranked with cohen mean,AFDN +141,142,MOBP,100-150,de-enriched marker for vip GABAergic cortical interneuron,markers ranked with cohen mean,MOBP +142,143,LINC01094,100-150,de-enriched marker for vip GABAergic cortical interneuron,markers ranked with cohen mean,LINC01094 +143,144,SAMD9L,100-150,de-enriched marker for vip GABAergic cortical interneuron,markers ranked with cohen mean,SAMD9L +144,145,ALDH1L1,1-50,known marker gene for astrocyte of the cerebral cortex,sourced from prior knowledge,ALDH1L1 +145,146,MBP,50-100,known marker gene for oligodendrocyte,sourced from prior knowledge,MBP +146,147,GFAP,50-100,known marker gene for astrocyte,sourced from prior knowledge,GFAP +147,148,AQP4,1-50,known marker gene for astrocyte,sourced from prior knowledge,AQP4 +148,149,PVALB,50-100,spcific marker for pvalb interneurons,sourced from prior knowledge,PVALB +149,150,SST,1-50,known marker gene SST interneurons,sourced from prior knowledge,SST diff --git a/panel_design/split/4_top50.csv b/panel_design/split/4_top50.csv new file mode 100644 index 0000000000000000000000000000000000000000..216eac141c383b4706aeb452c7d967d4825f480e --- /dev/null +++ b/panel_design/split/4_top50.csv @@ -0,0 +1,51 @@ +Unnamed: 0.1,Unnamed: 0,Gene.Symbol,Ranking,Annotation...reasoning,Additional.note,Gene Symbol +0,1,FSTL4,1-50,More distinct marker than L5,markers ranked with cohen mean,FSTL4 +1,2,SATB2,1-50,abundant marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean,SATB2 +2,3,KCNIP4,1-50,abundant marker for L2/3-6 intratelencephalic projecting glutamatergic neuron,markers ranked with cohen mean,KCNIP4 +3,4,TAFA1,1-50,abundant marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean,TAFA1 +4,5,VAT1L,1-50,abundant marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean,VAT1L +5,6,CBLN2,1-50,abundant marker for L5 extratelencephalic projecting glutamatergic cortical neuron,markers ranked with cohen mean,CBLN2 +6,7,ARPP21,1-50,abundant marker for L6b glutamatergic cortical neuron,markers ranked with cohen mean,ARPP21 +7,8,RAD52,1-50,abundant marker for astrocyte of the cerebral cortex,markers ranked with cohen mean,RAD52 +8,9,PDK4,1-50,abundant marker for astrocyte of the cerebral cortex,markers ranked with cohen mean,PDK4 +9,10,SEMA3B,1-50,abundant marker for astrocyte of the cerebral cortex,markers ranked with cohen mean,SEMA3B +10,11,ADARB2,1-50,abundant marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,ADARB2 +11,12,SORCS3,1-50,abundant marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,SORCS3 +12,13,CXCL14,1-50,abundant marker for caudal ganglionic eminence derived GABAergic cortical interneuron,markers ranked with cohen mean,CXCL14 +13,14,MAD1L1,1-50,abundant marker for cerebral cortex endothelial cell,markers ranked with cohen mean,MAD1L1 +14,15,CYP26B1,1-50,abundant marker for cerebral cortex endothelial cell,markers ranked with cohen mean,CYP26B1 +15,16,CASP10,1-50,abundant marker for cerebral cortex endothelial cell,markers ranked with cohen mean,CASP10 +16,17,ZNF536,1-50,abundant marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,ZNF536 +17,18,ZNF385D,1-50,abundant marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,ZNF385D +18,19,THSD7A,1-50,abundant marker for chandelier pvalb GABAergic cortical interneuron,markers ranked with cohen mean,THSD7A +19,20,SEMA3E,1-50,abundant marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean,SEMA3E +20,21,EGFEM1P,1-50,abundant marker for corticothalamic-projecting glutamatergic cortical neuron,markers ranked with cohen mean,EGFEM1P +21,22,LAMP5,1-50,abundant marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean,LAMP5 +22,23,FGF13,1-50,abundant marker for lamp5 GABAergic cortical interneuron,markers ranked with cohen mean,FGF13 +23,24,C1orf112,1-50,abundant marker for microglial cell,markers ranked with cohen mean,C1orf112 +24,25,CEACAM21,1-50,abundant marker for microglial cell,markers ranked with cohen mean,CEACAM21 +25,26,TYROBP,1-50,abundant marker for microglial cell,markers ranked with cohen mean,TYROBP +26,27,TSHZ2,1-50,abundant marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean,TSHZ2 +27,28,HTR2C,1-50,abundant marker for near-projecting glutamatergic cortical neuron,markers ranked with cohen mean,HTR2C +28,29,GCFC2,1-50,abundant marker for oligodendrocyte,markers ranked with cohen mean,GCFC2 +29,30,LAMP2,1-50,abundant marker for oligodendrocyte,markers ranked with cohen mean,LAMP2 +30,31,TMEM98,1-50,abundant marker for oligodendrocyte,markers ranked with cohen mean,TMEM98 +31,32,HECW1,1-50,abundant marker for oligodendrocyte precursor cell,markers ranked with cohen mean,HECW1 +32,33,KLHL13,1-50,abundant marker for oligodendrocyte precursor cell,markers ranked with cohen mean,KLHL13 +33,34,ATP1A2,1-50,abundant marker for oligodendrocyte precursor cell,markers ranked with cohen mean,ATP1A2 +34,35,ABTB3,1-50,abundant marker for pvalb GABAergic cortical interneuron,markers ranked with cohen mean,ABTB3 +35,36,GCLC,1-50,abundant marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean,GCLC +36,37,HCCS,1-50,abundant marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean,HCCS +37,38,DPEP1,1-50,abundant marker for sncg GABAergic cortical interneuron,markers ranked with cohen mean,DPEP1 +38,39,SST,1-50,abundant marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,SST +39,40,GRIK1,1-50,abundant marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,GRIK1 +40,41,SYNPR,1-50,abundant marker for sst GABAergic cortical interneuron,markers ranked with cohen mean,SYNPR +41,42,ATP1A2,1-50,abundant marker for vascular leptomeningeal cell,markers ranked with cohen mean,ATP1A2 +42,43,EBF1,1-50,abundant marker for vascular leptomeningeal cell,markers ranked with cohen mean,EBF1 +43,44,PDGFRB,1-50,abundant marker for vascular leptomeningeal cell,markers ranked with cohen mean,PDGFRB +44,45,VIP,1-50,abundant marker for vip GABAergic cortical interneuron,markers ranked with cohen mean,VIP +45,46,GALNTL6,1-50,abundant marker for vip GABAergic cortical interneuron,markers ranked with cohen mean,GALNTL6 +46,47,CX3CR1,1-50,abundant marker for microglial cell,Known Marker,CX3CR1 +144,145,ALDH1L1,1-50,known marker gene for astrocyte of the cerebral cortex,sourced from prior knowledge,ALDH1L1 +147,148,AQP4,1-50,known marker gene for astrocyte,sourced from prior knowledge,AQP4 +149,150,SST,1-50,known marker gene SST interneurons,sourced from prior knowledge,SST diff --git a/panel_design/split/5_top100.csv b/panel_design/split/5_top100.csv new file mode 100644 index 0000000000000000000000000000000000000000..e25ec8fe4094a005ea6c0143a3b11443a091e27a --- /dev/null +++ b/panel_design/split/5_top100.csv @@ -0,0 +1,101 @@ +Unnamed: 0,Gene symbol,Ranking,Annotation & Reasoning,Gene Symbol +0,ADARB2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ADARB2 +1,ERBB4,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ERBB4 +2,ROBO2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ROBO2 +3,KCNIP4,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,KCNIP4 +4,DPP10,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,DPP10 +5,SGCZ,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SGCZ +6,PLP1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PLP1 +7,DCC,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,DCC +8,CNTN5,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CNTN5 +9,LINGO2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LINGO2 +10,PCDH9,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PCDH9 +11,KCNMB2-AS1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,KCNMB2-AS1 +12,PTPRT,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PTPRT +13,HS3ST4,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,HS3ST4 +14,PCDH9-AS2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PCDH9-AS2 +15,GALNTL6,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GALNTL6 +16,CDH12,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CDH12 +17,RELN,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,RELN +18,CCK,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CCK +19,GRID2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GRID2 +20,NTM,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NTM +21,CLDN5,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CLDN5 +22,LRP1B,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LRP1B +23,FTH1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,FTH1 +24,ROBO1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ROBO1 +25,PRKG1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PRKG1 +26,GPC6,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GPC6 +27,MGAT4C,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,MGAT4C +28,NLGN1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NLGN1 +29,CDH13,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CDH13 +30,ZNF804B,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ZNF804B +31,NKAIN2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NKAIN2 +32,BCYRN1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,BCYRN1 +33,NRG1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NRG1 +34,LRRTM4,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LRRTM4 +35,NCAM2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NCAM2 +36,PDE5A,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PDE5A +37,TSHZ2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,TSHZ2 +38,ARHGAP24,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ARHGAP24 +39,PCDH7,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PCDH7 +40,LINC00609,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LINC00609 +41,HS6ST3,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,HS6ST3 +42,TAFA2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,TAFA2 +43,SLC8A1-AS1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SLC8A1-AS1 +44,PDE4B,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PDE4B +45,TRPM3,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,TRPM3 +46,PDE1A,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PDE1A +47,SOX5,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SOX5 +48,GRIK1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GRIK1 +49,GAPDH,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GAPDH +50,EPHA6,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,EPHA6 +51,PEX5L,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PEX5L +52,PLXDC2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PLXDC2 +53,KIRREL3,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,KIRREL3 +54,UNC5D,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,UNC5D +55,CXCL14,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CXCL14 +56,FTL,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,FTL +57,MARCHF1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,MARCHF1 +58,CTNNA2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CTNNA2 +59,ASIC2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ASIC2 +60,LAMA2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LAMA2 +61,PCDH11Y,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PCDH11Y +62,SORCS3,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SORCS3 +63,SRGAP2-AS1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SRGAP2-AS1 +64,KAZN,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,KAZN +65,NPAS3,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NPAS3 +66,TOX,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,TOX +67,HFM1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,HFM1 +68,ALCAM,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ALCAM +69,SDK1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SDK1 +70,PPARGC1A,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PPARGC1A +71,SLC6A1-AS1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SLC6A1-AS1 +72,CDH20,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CDH20 +73,SLC5A11,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SLC5A11 +74,NELL1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NELL1 +75,DPP6,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,DPP6 +76,RPS27A,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,RPS27A +77,ITPR2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ITPR2 +78,ATP6V0C,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ATP6V0C +79,ZBTB20,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ZBTB20 +80,DPP10-AS3,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,DPP10-AS3 +81,CNTNAP2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CNTNAP2 +82,INPP4B,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,INPP4B +83,MOBP,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,MOBP +84,NTNG1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NTNG1 +85,GPC5,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GPC5 +86,PTPRK,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PTPRK +87,KCNH7,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,KCNH7 +88,SLIT2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SLIT2 +89,PCSK1N,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PCSK1N +90,UNC5C,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,UNC5C +91,APBB1IP,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,APBB1IP +92,RALYL,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,RALYL +93,LRRC4C,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LRRC4C +94,SPOCK3,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SPOCK3 +95,SGCD,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SGCD +96,ASTN2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ASTN2 +97,SST,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SST +98,NRXN1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NRXN1 +99,NRGN,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NRGN diff --git a/panel_design/split/5_top150.csv b/panel_design/split/5_top150.csv new file mode 100644 index 0000000000000000000000000000000000000000..a74d2c9052200dc86cb310a468b62092b893c0c8 --- /dev/null +++ b/panel_design/split/5_top150.csv @@ -0,0 +1,151 @@ +Unnamed: 0,Gene symbol,Ranking,Annotation & Reasoning,Gene Symbol +0,ADARB2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ADARB2 +1,ERBB4,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ERBB4 +2,ROBO2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ROBO2 +3,KCNIP4,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,KCNIP4 +4,DPP10,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,DPP10 +5,SGCZ,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SGCZ +6,PLP1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PLP1 +7,DCC,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,DCC +8,CNTN5,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CNTN5 +9,LINGO2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LINGO2 +10,PCDH9,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PCDH9 +11,KCNMB2-AS1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,KCNMB2-AS1 +12,PTPRT,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PTPRT +13,HS3ST4,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,HS3ST4 +14,PCDH9-AS2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PCDH9-AS2 +15,GALNTL6,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GALNTL6 +16,CDH12,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CDH12 +17,RELN,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,RELN +18,CCK,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CCK +19,GRID2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GRID2 +20,NTM,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NTM +21,CLDN5,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CLDN5 +22,LRP1B,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LRP1B +23,FTH1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,FTH1 +24,ROBO1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ROBO1 +25,PRKG1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PRKG1 +26,GPC6,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GPC6 +27,MGAT4C,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,MGAT4C +28,NLGN1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NLGN1 +29,CDH13,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CDH13 +30,ZNF804B,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ZNF804B +31,NKAIN2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NKAIN2 +32,BCYRN1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,BCYRN1 +33,NRG1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NRG1 +34,LRRTM4,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LRRTM4 +35,NCAM2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NCAM2 +36,PDE5A,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PDE5A +37,TSHZ2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,TSHZ2 +38,ARHGAP24,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ARHGAP24 +39,PCDH7,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PCDH7 +40,LINC00609,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LINC00609 +41,HS6ST3,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,HS6ST3 +42,TAFA2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,TAFA2 +43,SLC8A1-AS1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SLC8A1-AS1 +44,PDE4B,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PDE4B +45,TRPM3,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,TRPM3 +46,PDE1A,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PDE1A +47,SOX5,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SOX5 +48,GRIK1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GRIK1 +49,GAPDH,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GAPDH +50,EPHA6,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,EPHA6 +51,PEX5L,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PEX5L +52,PLXDC2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PLXDC2 +53,KIRREL3,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,KIRREL3 +54,UNC5D,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,UNC5D +55,CXCL14,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CXCL14 +56,FTL,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,FTL +57,MARCHF1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,MARCHF1 +58,CTNNA2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CTNNA2 +59,ASIC2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ASIC2 +60,LAMA2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LAMA2 +61,PCDH11Y,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PCDH11Y +62,SORCS3,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SORCS3 +63,SRGAP2-AS1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SRGAP2-AS1 +64,KAZN,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,KAZN +65,NPAS3,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NPAS3 +66,TOX,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,TOX +67,HFM1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,HFM1 +68,ALCAM,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ALCAM +69,SDK1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SDK1 +70,PPARGC1A,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PPARGC1A +71,SLC6A1-AS1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SLC6A1-AS1 +72,CDH20,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CDH20 +73,SLC5A11,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SLC5A11 +74,NELL1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NELL1 +75,DPP6,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,DPP6 +76,RPS27A,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,RPS27A +77,ITPR2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ITPR2 +78,ATP6V0C,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ATP6V0C +79,ZBTB20,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ZBTB20 +80,DPP10-AS3,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,DPP10-AS3 +81,CNTNAP2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CNTNAP2 +82,INPP4B,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,INPP4B +83,MOBP,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,MOBP +84,NTNG1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NTNG1 +85,GPC5,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GPC5 +86,PTPRK,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PTPRK +87,KCNH7,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,KCNH7 +88,SLIT2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SLIT2 +89,PCSK1N,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PCSK1N +90,UNC5C,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,UNC5C +91,APBB1IP,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,APBB1IP +92,RALYL,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,RALYL +93,LRRC4C,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LRRC4C +94,SPOCK3,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SPOCK3 +95,SGCD,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SGCD +96,ASTN2,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ASTN2 +97,SST,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SST +98,NRXN1,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NRXN1 +99,NRGN,top 50-100,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NRGN +100,DOCK8,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,DOCK8 +101,GRM3,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GRM3 +102,LRRTM3,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LRRTM3 +103,KCNQ5,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,KCNQ5 +104,VIP,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,VIP +105,UBE3A,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,UBE3A +106,RAPGEF5,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,RAPGEF5 +107,CNTN4,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CNTN4 +108,GLIS3,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GLIS3 +109,RPL26,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,RPL26 +110,NCKAP5,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NCKAP5 +111,GRIA4,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GRIA4 +112,LEF1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LEF1 +113,TMTC2,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,TMTC2 +114,RGS6,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,RGS6 +115,DPYD,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,DPYD +116,PLCL1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PLCL1 +117,TUBB2A,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,TUBB2A +118,SOX2-OT,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SOX2-OT +119,PDE1C,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PDE1C +120,QKI,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,QKI +121,EDIL3,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,EDIL3 +122,TAFA1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,TAFA1 +123,SYT1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SYT1 +124,MAML2,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,MAML2 +125,SLC8A1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SLC8A1 +126,TENM2,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,TENM2 +127,DSCAML1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,DSCAML1 +128,BCAS1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,BCAS1 +129,FAM177B,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,FAM177B +130,CSGALNACT1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CSGALNACT1 +131,ARHGAP26,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ARHGAP26 +132,ATRNL1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ATRNL1 +133,EEF1A1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,EEF1A1 +134,CNTNAP4,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CNTNAP4 +135,ST18,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ST18 +136,HPSE2,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,HPSE2 +137,DLC1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,DLC1 +138,IL1RAPL1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,IL1RAPL1 +139,ZNF536,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ZNF536 +140,CHST11,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CHST11 +141,DAB1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,DAB1 +142,CALM1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CALM1 +143,DGKB,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,DGKB +144,ST6GALNAC3,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ST6GALNAC3 +145,KCNQ3,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,KCNQ3 +146,DSCAM,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,DSCAM +147,SYNJ2,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SYNJ2 +148,FHIT,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,FHIT +149,SAMSN1,top 100-150,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SAMSN1 diff --git a/panel_design/split/5_top50.csv b/panel_design/split/5_top50.csv new file mode 100644 index 0000000000000000000000000000000000000000..10f7d3b375f6fb6473aab855ec18f46cec934f8a --- /dev/null +++ b/panel_design/split/5_top50.csv @@ -0,0 +1,51 @@ +Unnamed: 0,Gene symbol,Ranking,Annotation & Reasoning,Gene Symbol +0,ADARB2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ADARB2 +1,ERBB4,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ERBB4 +2,ROBO2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ROBO2 +3,KCNIP4,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,KCNIP4 +4,DPP10,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,DPP10 +5,SGCZ,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SGCZ +6,PLP1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PLP1 +7,DCC,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,DCC +8,CNTN5,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CNTN5 +9,LINGO2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LINGO2 +10,PCDH9,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PCDH9 +11,KCNMB2-AS1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,KCNMB2-AS1 +12,PTPRT,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PTPRT +13,HS3ST4,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,HS3ST4 +14,PCDH9-AS2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PCDH9-AS2 +15,GALNTL6,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GALNTL6 +16,CDH12,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CDH12 +17,RELN,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,RELN +18,CCK,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CCK +19,GRID2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GRID2 +20,NTM,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NTM +21,CLDN5,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CLDN5 +22,LRP1B,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LRP1B +23,FTH1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,FTH1 +24,ROBO1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ROBO1 +25,PRKG1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PRKG1 +26,GPC6,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GPC6 +27,MGAT4C,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,MGAT4C +28,NLGN1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NLGN1 +29,CDH13,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,CDH13 +30,ZNF804B,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ZNF804B +31,NKAIN2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NKAIN2 +32,BCYRN1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,BCYRN1 +33,NRG1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NRG1 +34,LRRTM4,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LRRTM4 +35,NCAM2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,NCAM2 +36,PDE5A,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PDE5A +37,TSHZ2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,TSHZ2 +38,ARHGAP24,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,ARHGAP24 +39,PCDH7,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PCDH7 +40,LINC00609,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,LINC00609 +41,HS6ST3,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,HS6ST3 +42,TAFA2,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,TAFA2 +43,SLC8A1-AS1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SLC8A1-AS1 +44,PDE4B,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PDE4B +45,TRPM3,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,TRPM3 +46,PDE1A,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,PDE1A +47,SOX5,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,SOX5 +48,GRIK1,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GRIK1 +49,GAPDH,top 50,gene selected by an iterative greedy algorithm for reconstruction of the kNN graph of the reference scRNA-seq data,GAPDH diff --git a/panel_design/split/6_top100.csv b/panel_design/split/6_top100.csv new file mode 100644 index 0000000000000000000000000000000000000000..9a2d8cfdae5bb959449d5ac0b21735c378b155f8 --- /dev/null +++ b/panel_design/split/6_top100.csv @@ -0,0 +1,101 @@ +Unnamed: 0,symbol,Ranking,Annotation & Reasoning,ensembl_id,cellType.target,mean.target,cellType,mean,ratio,rank_ratio,anno_ratio,logFC,log.p.value,log.FDR,std.logFC,rank_marker,anno_logFC,Unnamed: 17,cellTypeResolution,Gene Symbol +0,BTBD11,1,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000151136,Inhib,2.607133345,OPC,0.3050867244,8.545548319,7,Inhib/OPC: 8.546,2.221299082,-22165.77242,-22155.57679,2.991557876,1, std logFC = 2.992,,broad,BTBD11 +1,ST18,2,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000147488,Oligo,4.651734362,Micro,0.3638996875,12.78301279,11,Oligo/Micro: 12.783,4.465531379,-38170.35,-38160.15437,4.392440029,1, std logFC = 4.392,,broad,ST18 +2,AC004852.2,3,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000278254,OPC,3.198955525,Inhib,0.06788469054,47.12337199,1,OPC/Inhib: 47.123,3.162152196,-34123.87078,-34113.67514,8.5255685,1, std logFC = 8.526,,broad,AC004852.2 +3,OBI1-AS1,4,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000234377,Astro,3.758269422,Oligo,0.3138484541,11.97479029,2,Astro/Oligo: 11.975,3.571921082,-22878.94594,-22868.75031,4.389697553,1, std logFC = 4.39,,broad,OBI1-AS1 +4,ITIH5,5,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000123243,EndoMural,3.034600494,Micro,0.0843586809,35.972593,2,EndoMural/Micro: 35.973,2.979076489,-24405.49833,-24395.3027,6.140134848,1, std logFC = 6.14,,broad,ITIH5 +5,DOCK8,6,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000107099,Micro,3.89732526,EndoMural,0.2363190559,16.4917943,19,Micro/EndoMural: 16.492,3.849979809,-33550.52388,-33540.32824,9.123545355,1, std logFC = 9.124,,broad,DOCK8 +6,BTBD11,7,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000151136,Inhib,2.607133345,Excit_L2/3,0.4583811315,5.687697783,4,Inhib/Excit_L2/3: 5.688,2.232219442,-21879.15743,-21868.96179,3.009130469,1, std logFC = 3.009,,layer,BTBD11 +7,ST18,8,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000147488,Oligo,4.651734362,Micro,0.3638996875,12.78301279,11,Oligo/Micro: 12.783,4.498617988,-37842.74672,-37832.55109,4.45769983,1, std logFC = 4.458,,layer,ST18 +8,AC004852.2,9,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000278254,OPC,3.198955525,Inhib,0.06788469054,47.12337199,1,OPC/Inhib: 47.123,3.161932798,-33191.99065,-33181.79502,8.447310226,1, std logFC = 8.447,,layer,AC004852.2 +9,MAP1B,10,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000131711,Excit_L3/4/5,5.481322199,Excit_L4,4.859234787,1.128021682,25,Excit_L3/4/5/Excit_L4: 1.128,2.357513634,-3728.573791,-3718.378156,1.697613701,1, std logFC = 1.698,,layer,MAP1B +10,CBLN2,11,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000141668,Excit_L3,2.326846695,Excit_L5/6,1.692531181,1.37477331,21,Excit_L3/Excit_L5/6: 1.375,1.884852238,-12389.93168,-12379.73605,1.969356146,1, std logFC = 1.969,,layer,CBLN2 +11,OBI1-AS1,12,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000234377,Astro,3.758269422,Oligo,0.3138484541,11.97479029,2,Astro/Oligo: 11.975,3.59279821,-24845.60054,-24835.4049,4.724479205,1, std logFC = 4.724,,layer,OBI1-AS1 +12,ITIH5,13,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000123243,EndoMural,3.034600494,Excit_L3/4/5,0.1202223609,25.24156465,3,EndoMural/Excit_L3/4/5: 25.242,2.982326922,-24223.80788,-24213.61225,6.170504958,1, std logFC = 6.171,,layer,ITIH5 +13,DOCK8,14,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000107099,Micro,3.89732526,EndoMural,0.2363190559,16.4917943,17,Micro/EndoMural: 16.492,3.849829499,-32837.07734,-32826.8817,9.082881361,1, std logFC = 9.083,,layer,DOCK8 +14,MCTP2,15,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000140563,Excit_L6,1.145245232,EndoMural,0.1418083509,8.076006977,2,Excit_L6/EndoMural: 8.076,1.102705535,-6974.182921,-6963.987287,3.03953067,1, std logFC = 3.04,,layer,MCTP2 +15,THEMIS,16,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000172673,Excit_L5/6,1.180947014,Excit_L5,0.4727839547,2.497857641,2,Excit_L5/6/Excit_L5: 2.498,1.046802894,-4183.521725,-4173.326091,1.965745525,1, std logFC = 1.966,,layer,THEMIS +16,AP003066.1,17,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000254587,Excit_L5,0.9709158406,Excit_L4,0.291616737,3.329424266,1,Excit_L5/Excit_L4: 3.329,0.9097670434,-7118.396732,-7108.201098,2.6322311,1, std logFC = 2.632,,layer,AP003066.1 +17,GAD2,18,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000136750,Inhib,2.326517424,Oligo,0.1658070258,14.03147673,3,Inhib/Oligo: 14.031,2.115583238,-20977.67585,-20968.17336,2.875964071,2, std logFC = 2.876,,broad,GAD2 +18,PDGFRA,19,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000134853,OPC,2.708879833,EndoMural,0.1486911615,18.21816311,4,OPC/EndoMural: 18.218,2.660844387,-24926.02107,-24916.51858,6.623062703,2, std logFC = 6.623,,broad,PDGFRA +19,CABP1,20,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000157782,Excit,2.510583463,Oligo,0.504915941,4.972280055,21,Excit/Oligo: 4.972,1.913232828,-17212.32586,-17202.82338,1.918615179,2, std logFC = 1.919,,broad,CABP1 +20,ADGRV1,21,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164199,Astro,4.473246084,Excit,0.5158270259,8.671988591,6,Astro/Excit: 8.672,3.978323448,-20881.17828,-20871.67579,4.110730183,2, std logFC = 4.111,,broad,ADGRV1 +21,EBF1,22,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164330,EndoMural,3.366858494,Inhib,0.1538390138,21.88559593,7,EndoMural/Inhib: 21.886,3.28545377,-19807.44179,-19797.9393,5.282737171,2, std logFC = 5.283,,broad,EBF1 +22,APBB1IP,23,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000077420,Micro,3.82802585,EndoMural,0.1379134818,27.75671966,12,Micro/EndoMural: 27.757,3.785317824,-33045.61491,-33036.11242,9.006461122,2, std logFC = 9.006,,broad,APBB1IP +23,GAD2,24,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000136750,Inhib,2.326517424,Excit_L2/3,0.6869905244,3.386534954,9,Inhib/Excit_L2/3: 3.387,2.117471009,-21035.58962,-21026.08713,2.924786644,2, std logFC = 2.925,,layer,GAD2 +24,PDGFRA,25,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000134853,OPC,2.708879833,EndoMural,0.1486911615,18.21816311,4,OPC/EndoMural: 18.218,2.661366083,-24639.22108,-24629.7186,6.636112056,2, std logFC = 6.636,,layer,PDGFRA +25,CALM1,26,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198668,Excit_L3/4/5,5.74028179,Excit_L4,4.987590797,1.15091274,15,Excit_L3/4/5/Excit_L4: 1.151,2.354909866,-3489.442816,-3479.940328,1.638514659,2, std logFC = 1.639,,layer,CALM1 +26,CUX2,27,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000111249,Excit_L3,2.400736547,Inhib,1.535578791,1.563408248,7,Excit_L3/Inhib: 1.563,1.969430629,-12347.48171,-12337.97923,1.965153047,2, std logFC = 1.965,,layer,CUX2 +27,ADGRV1,28,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164199,Astro,4.473246084,Excit_L3,0.5998035463,7.457852011,6,Astro/Excit_L3: 7.458,3.997336738,-20967.08141,-20957.57892,4.167294033,2, std logFC = 4.167,,layer,ADGRV1 +28,EBF1,29,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164330,EndoMural,3.366858494,Inhib,0.1538390138,21.88559593,5,EndoMural/Inhib: 21.886,3.297611891,-21376.38612,-21366.88363,5.626266372,2, std logFC = 5.626,,layer,EBF1 +29,APBB1IP,30,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000077420,Micro,3.82802585,EndoMural,0.1379134818,27.75671966,11,Micro/EndoMural: 27.757,3.786556774,-32457.1598,-32447.65732,8.992548136,2, std logFC = 8.993,,layer,APBB1IP +30,AC099517.1,31,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000287176,Excit_L5/6,1.096716549,Excit_L5,0.7892758353,1.389522522,22,Excit_L5/6/Excit_L5: 1.39,0.9844434124,-4108.21197,-4098.709483,1.94656857,2, std logFC = 1.947,,layer,AC099517.1 +31,AC073091.3,32,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000287172,Excit_L5,2.799017012,Excit_L5/6,1.732461721,1.615629932,14,Excit_L5/Excit_L5/6: 1.616,2.240268387,-5465.523097,-5456.02061,2.270115933,2, std logFC = 2.27,,layer,AC073091.3 +32,MOBP,33,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000168314,Oligo,3.538881555,Micro,0.3821774358,9.259786749,19,Oligo/Micro: 9.26,3.2201278,-27951.86856,-27942.77154,3.37455489,3, std logFC = 3.375,,broad,MOBP +33,MEGF11,34,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000157890,OPC,3.351998122,Inhib,0.2164735628,15.48456116,5,OPC/Inhib: 15.485,3.22097198,-24488.41936,-24479.32234,6.535601574,3, std logFC = 6.536,,broad,MEGF11 +34,ADAM28,35,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000042980,Micro,3.005809501,Inhib,0.1116458658,26.92271209,14,Micro/Inhib: 26.923,2.953040163,-26207.24442,-26198.1474,7.470789811,3, std logFC = 7.471,,broad,ADAM28 +35,GAD1,36,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000128683,Inhib,2.370257819,OPC,0.9781879376,2.423110864,20,Inhib/OPC: 2.423,2.134891009,-20843.6985,-20834.60148,2.905628895,3, std logFC = 2.906,,layer,GAD1 +36,MOBP,37,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000168314,Oligo,3.538881555,Excit_L2/3,0.429038715,8.248396779,19,Oligo/Excit_L2/3: 8.248,3.264762216,-28589.15327,-28580.05624,3.498833224,3, std logFC = 3.499,,layer,MOBP +37,MEGF11,38,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000157890,OPC,3.351998122,Excit_L6,0.4393208706,7.6299542,8,OPC/Excit_L6: 7.63,3.221269673,-24076.45133,-24067.35431,6.521045022,3, std logFC = 6.521,,layer,MEGF11 +38,TUBA1B,39,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000123416,Excit_L3/4/5,3.856794784,Excit_L4,3.259121796,1.183384674,10,Excit_L3/4/5/Excit_L4: 1.183,2.152929616,-3376.611792,-3367.51477,1.610062965,3, std logFC = 1.61,,layer,TUBA1B +39,TSHZ2,40,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000182463,Excit_L4,2.513501671,Excit_L5,1.815381111,1.384558678,1,Excit_L4/Excit_L5: 1.385,2.041678543,-3525.994817,-3516.897794,1.825827726,3, std logFC = 1.826,,layer,TSHZ2 +40,AL137139.2,41,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000286757,Astro,2.750399175,EndoMural,0.7166476389,3.837868188,20,Astro/EndoMural: 3.838,2.595534945,-17585.73933,-17576.64231,3.686137516,3, std logFC = 3.686,,layer,AL137139.2 +41,EPAS1,42,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000116016,EndoMural,3.286727061,OPC,0.583780088,5.630077368,24,EndoMural/OPC: 5.63,3.13899098,-18569.36355,-18560.26653,5.094193063,3, std logFC = 5.094,,layer,EPAS1 +42,ADAM28,43,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000042980,Micro,3.005809501,Inhib,0.1116458658,26.92271209,13,Micro/Inhib: 26.923,2.953090913,-25692.73878,-25683.64175,7.436880268,3, std logFC = 7.437,,layer,ADAM28 +43,LINC00343,44,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000226620,Excit_L5/6,0.6838777434,Excit_L4,0.1966485482,3.477664848,1,Excit_L5/6/Excit_L4: 3.478,0.612425043,-3815.624154,-3806.527131,1.870723949,3, std logFC = 1.871,,layer,LINC00343 +44,AL033539.2,45,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000286954,Excit_L5,0.5831326126,Excit_L4,0.2623003828,2.223148157,4,Excit_L5/Excit_L4: 2.223,0.5387135463,-5367.091019,-5357.993996,2.247459805,3, std logFC = 2.247,,layer,AL033539.2 +45,GRIP2,46,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000144596,Inhib,1.454320477,EndoMural,0.1302827841,11.16279857,6,Inhib/EndoMural: 11.163,1.294597378,-18824.6624,-18815.85306,2.666933752,4, std logFC = 2.667,,broad,GRIP2 +46,BX284613.2,47,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000231424,OPC,3.206382317,EndoMural,0.496363375,6.459747995,13,OPC/EndoMural: 6.46,3.113238963,-23593.02996,-23584.22062,6.357212581,4, std logFC = 6.357,,broad,BX284613.2 +47,LINC00299,48,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000236790,Astro,3.197232057,Excit,0.4970508863,6.432403895,11,Astro/Excit: 6.432,2.843051279,-15690.46465,-15681.65531,3.386312678,4, std logFC = 3.386,,broad,LINC00299 +48,FLT1,49,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000102755,EndoMural,3.250079224,Micro,0.2425572587,13.39922475,12,EndoMural/Micro: 13.399,3.128544555,-15945.16946,-15936.36012,4.563792751,4, std logFC = 4.564,,broad,FLT1 +49,TBXAS1,50,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000059377,Micro,2.968864785,Astro,0.1011493705,29.35129273,10,Micro/Astro: 29.351,2.920644873,-24296.42317,-24287.61383,7.054872707,4, std logFC = 7.055,,broad,TBXAS1 +50,ZNF385D,51,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000151789,Inhib,3.711723082,Excit_L2/3,1.636083675,2.268663357,23,Inhib/Excit_L2/3: 2.269,3.094519038,-19109.90017,-19101.09083,2.732838004,4, std logFC = 2.733,,layer,ZNF385D +51,VCAN,52,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000038427,OPC,4.239971898,Astro,0.6973526945,6.080096817,14,OPC/Astro: 6.08,4.093962541,-23078.97859,-23070.16925,6.317945452,4, std logFC = 6.318,,layer,VCAN +52,STMN2,53,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000104435,Excit_L3/4/5,3.558073538,Excit_L4,3.151266501,1.129093187,24,Excit_L3/4/5/Excit_L4: 1.129,2.066072579,-2987.657537,-2978.848197,1.508844291,4, std logFC = 1.509,,layer,STMN2 +53,FLT1,54,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000102755,EndoMural,3.250079224,Micro,0.2425572587,13.39922475,10,EndoMural/Micro: 13.399,3.135745883,-15914.36183,-15905.55249,4.590363107,4, std logFC = 4.59,,layer,FLT1 +54,TBXAS1,55,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000059377,Micro,2.968864785,Astro,0.1011493705,29.35129273,9,Micro/Astro: 29.351,2.920646171,-23800.559,-23791.74966,7.016004295,4, std logFC = 7.016,,layer,TBXAS1 +55,AC019211.1,56,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000239498,Excit_L5/6,2.768667305,Excit_L3,1.951978314,1.418390402,19,Excit_L5/6/Excit_L3: 1.418,1.936861229,-3285.072502,-3276.263162,1.72699297,4, std logFC = 1.727,,layer,AC019211.1 +56,TLL1,57,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000038295,Excit_L5,1.566350741,Excit_L5/6,0.7142612438,2.192966165,5,Excit_L5/Excit_L5/6: 2.193,1.413459313,-5326.778106,-5317.968765,2.238138652,4, std logFC = 2.238,,layer,TLL1 +57,TF,58,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000091513,Oligo,3.281925193,Astro,0.4361364794,7.524995838,22,Oligo/Astro: 7.525,2.974140605,-25258.08742,-25249.50122,3.120593098,5, std logFC = 3.121,,broad,TF +58,VCAN,59,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000038427,OPC,4.239971898,Astro,0.6973526945,6.080096817,15,OPC/Astro: 6.08,4.093854266,-23366.01208,-23357.42589,6.312093879,5, std logFC = 6.312,,broad,VCAN +59,PRDM16,60,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000142611,Astro,1.965483537,EndoMural,0.1415356302,13.88684626,1,Astro/EndoMural: 13.887,1.890689945,-15153.31748,-15144.73129,3.31059695,5, std logFC = 3.311,,broad,PRDM16 +60,COBLL1,61,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000082438,EndoMural,3.535001711,Excit,0.3801767269,9.29831171,17,EndoMural/Excit: 9.298,3.273943496,-15446.89134,-15438.30514,4.470288074,5, std logFC = 4.47,,broad,COBLL1 +61,CSF2RA,62,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198223,Micro,2.489448328,EndoMural,0.03987363654,62.43344085,5,Micro/EndoMural: 62.433,2.464231696,-23546.26297,-23537.67677,6.892680072,5, std logFC = 6.893,,broad,CSF2RA +62,GRIP2,63,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000144596,Inhib,1.454320477,Excit_L2/3,0.41529399,3.501905907,8,Inhib/Excit_L2/3: 3.502,1.297473899,-18670.68231,-18662.09611,2.689107384,5, std logFC = 2.689,,layer,GRIP2 +63,TF,64,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000091513,Oligo,3.281925193,Astro,0.4361364794,7.524995838,21,Oligo/Astro: 7.525,3.031333133,-26430.26489,-26421.6787,3.287157931,5, std logFC = 3.287,,layer,TF +64,BX284613.2,65,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000231424,OPC,3.206382317,EndoMural,0.496363375,6.459747995,12,OPC/EndoMural: 6.46,3.111097484,-22884.31923,-22875.73304,6.278424716,5, std logFC = 6.278,,layer,BX284613.2 +65,CALM3,66,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000160014,Excit_L3/4/5,3.709015615,Excit_L4,3.223571557,1.150591991,16,Excit_L3/4/5/Excit_L4: 1.151,1.985257658,-2909.569992,-2900.983795,1.487876047,5, std logFC = 1.488,,layer,CALM3 +66,AC092957.1,67,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000243620,EndoMural,2.16355825,Astro,0.1154847537,18.73457908,6,EndoMural/Astro: 18.735,2.136118087,-15352.65638,-15344.07018,4.48316734,5, std logFC = 4.483,,layer,AC092957.1 +67,CSF2RA,68,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198223,Micro,2.489448328,Excit_L2/3,0.04203762655,59.21952623,5,Micro/Excit_L2/3: 59.22,2.464479819,-23024.7494,-23016.1632,6.844712531,5, std logFC = 6.845,,layer,CSF2RA +68,LINC02718,69,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000255418,Excit_L6,0.6996451567,EndoMural,0.06321257245,11.06813296,1,Excit_L6/EndoMural: 11.068,0.6548837902,-4351.073982,-4342.487785,2.341138402,5, std logFC = 2.341,,layer,LINC02718 +69,CASC15,70,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000272168,Excit_L5,3.445054998,Excit_L4,2.00686097,1.716638596,13,Excit_L5/Excit_L4: 1.717,2.561174412,-5282.18967,-5273.603473,2.227799686,5, std logFC = 2.228,,layer,CASC15 +70,ENPP2,71,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000136960,Oligo,2.833830413,OPC,0.2844518545,9.962425514,15,Oligo/OPC: 9.962,2.707451245,-24995.76515,-24987.36127,3.096082136,6, std logFC = 3.096,,broad,ENPP2 +71,LHFPL3,72,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000187416,OPC,5.943482667,Inhib,0.8290598316,7.168942988,10,OPC/Inhib: 7.169,5.602913607,-20162.95467,-20154.5508,5.678810335,6, std logFC = 5.679,,broad,LHFPL3 +72,AC092957.1,73,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000243620,EndoMural,2.16355825,Astro,0.1154847537,18.73457908,8,EndoMural/Astro: 18.735,2.134194384,-15379.8881,-15371.48422,4.457692082,6, std logFC = 4.458,,broad,AC092957.1 +73,FYB1,74,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000082074,Micro,2.551032802,EndoMural,0.1481983393,17.2136396,18,Micro/EndoMural: 17.214,2.523305139,-21189.15714,-21180.75326,6.385918489,6, std logFC = 6.386,,broad,FYB1 +74,LHFPL3,75,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000187416,OPC,5.943482667,Inhib,0.8290598316,7.168942988,9,OPC/Inhib: 7.169,5.625539581,-20159.08824,-20150.68437,5.728066973,6, std logFC = 5.728,,layer,LHFPL3 +75,NORAD,76,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000260032,Excit_L3/4/5,3.350699442,Excit_L4,2.841384123,1.17924902,11,Excit_L3/4/5/Excit_L4: 1.179,1.867739455,-2904.971247,-2896.567372,1.486633854,6, std logFC = 1.487,,layer,NORAD +76,AC008574.1,77,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000251293,Excit_L3,0.9575423718,Excit_L2/3,0.4575828704,2.092609741,2,Excit_L3/Excit_L2/3: 2.093,0.8900419455,-10798.82644,-10790.42256,1.809927618,6, std logFC = 1.81,,layer,AC008574.1 +77,PRDM16,78,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000142611,Astro,1.965483537,EndoMural,0.1415356302,13.88684626,1,Astro/EndoMural: 13.887,1.893996413,-14914.85085,-14906.44697,3.304215312,6, std logFC = 3.304,,layer,PRDM16 +78,FYB1,79,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000082074,Micro,2.551032802,EndoMural,0.1481983393,17.2136396,16,Micro/EndoMural: 17.214,2.523447889,-20643.88812,-20635.48424,6.322406089,6, std logFC = 6.322,,layer,FYB1 +79,ADAMTSL1,80,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000178031,Excit_L6,2.377115062,Excit_L5,0.9454280519,2.514326773,6,Excit_L6/Excit_L5: 2.514,2.008588364,-4203.428163,-4195.024288,2.297825729,6, std logFC = 2.298,,layer,ADAMTSL1 +80,ANK1,81,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000029534,Inhib,1.640331625,Excit,0.2162203098,7.586390134,10,Inhib/Excit: 7.586,1.474123431,-16081.11571,-16072.86598,2.399709636,7, std logFC = 2.4,,broad,ANK1 +81,FERMT1,82,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000101311,OPC,1.722814414,EndoMural,0.04082874593,42.1961139,2,OPC/EndoMural: 42.196,1.692500655,-18984.97429,-18976.72456,5.446649042,7, std logFC = 5.447,,broad,FERMT1 +82,MLIP,83,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000146147,Excit,1.646129521,Oligo,0.2236439769,7.360491186,10,Excit/Oligo: 7.36,1.419368353,-12476.19844,-12467.94872,1.560524131,7, std logFC = 1.561,,broad,MLIP +83,GLI3,84,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000106571,Astro,1.974149301,EndoMural,0.407848692,4.840396305,18,Astro/EndoMural: 4.84,1.886689057,-14289.69413,-14281.44441,3.18822336,7, std logFC = 3.188,,broad,GLI3 +84,ATP10A,85,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000206190,EndoMural,2.90391912,Excit,0.1122570077,25.86848855,5,EndoMural/Excit: 25.868,2.803930781,-15174.06071,-15165.81099,4.418962316,7, std logFC = 4.419,,broad,ATP10A +85,ANK1,86,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000029534,Inhib,1.640331625,Excit_L2/3,0.6175970678,2.655989983,14,Inhib/Excit_L2/3: 2.656,1.50843475,-17034.06009,-17025.81037,2.526017253,7, std logFC = 2.526,,layer,ANK1 +86,ENPP2,87,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000136960,Oligo,2.833830413,OPC,0.2844518545,9.962425514,14,Oligo/OPC: 9.962,2.734614411,-25019.02205,-25010.77233,3.150591195,7, std logFC = 3.151,,layer,ENPP2 +87,COL9A1,88,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000112280,OPC,1.788626477,EndoMural,0.09293992573,19.24497425,3,OPC/EndoMural: 19.245,1.762865747,-18536.14412,-18527.8944,5.401756121,7, std logFC = 5.402,,layer,COL9A1 +88,GLI3,89,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000106571,Astro,1.974149301,EndoMural,0.407848692,4.840396305,13,Astro/EndoMural: 4.84,1.886750458,-13995.79917,-13987.54944,3.171460728,7, std logFC = 3.171,,layer,GLI3 +89,ATP10A,90,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000206190,EndoMural,2.90391912,Excit_L6,0.2181010003,13.31456122,11,EndoMural/Excit_L6: 13.315,2.806470362,-14997.12634,-14988.87662,4.415139852,7, std logFC = 4.415,,layer,ATP10A +90,C3,91,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000125730,Micro,2.52206604,Oligo,0.07219728309,34.93297714,7,Micro/Oligo: 34.933,2.485800545,-20240.83407,-20232.58435,6.234341526,7, std logFC = 6.234,,layer,C3 +91,COL9A1,92,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000112280,OPC,1.788626477,EndoMural,0.09293992573,19.24497425,3,OPC/EndoMural: 19.245,1.762132666,-18731.56562,-18723.44942,5.396693614,8, std logFC = 5.397,,broad,COL9A1 +92,CARMN,93,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000249669,EndoMural,1.643565406,Oligo,0.0263563217,62.35943788,1,EndoMural/Oligo: 62.359,1.627501258,-14393.44504,-14385.32884,4.271525569,8, std logFC = 4.272,,broad,CARMN +93,LINC01374,94,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000280560,Micro,2.665601597,Inhib,0.08852780987,30.11033031,9,Micro/Inhib: 30.11,2.614257162,-19845.10532,-19836.98913,6.098132232,8, std logFC = 6.098,,broad,LINC01374 +94,TMEM144,95,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164124,Oligo,3.011743854,Astro,0.5404121759,5.573049588,24,Oligo/Astro: 5.573,2.813875241,-23039.42418,-23031.30798,2.9609775,8, std logFC = 2.961,,layer,TMEM144 +95,FERMT1,96,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000101311,OPC,1.722814414,Excit_L5,0.04741369715,36.33579572,2,OPC/Excit_L5: 36.336,1.693277281,-18530.83724,-18522.72105,5.400689362,8, std logFC = 5.401,,layer,FERMT1 +96,ABCG2,97,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000118777,EndoMural,2.223753949,Micro,0.1416969614,15.69373067,8,EndoMural/Micro: 15.694,2.171291149,-14281.57978,-14273.46359,4.277720977,8, std logFC = 4.278,,layer,ABCG2 +97,AC109466.1,98,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000241956,Excit_L5,2.875408149,Excit_L5/6,1.64483523,1.748143582,11,Excit_L5/Excit_L5/6: 1.748,2.381282963,-4736.240644,-4728.124451,2.098532254,8, std logFC = 2.099,,layer,AC109466.1 +98,STK32A,99,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000169302,OPC,2.019957136,Astro,0.1383572451,14.59957616,6,OPC/Astro: 14.6,1.957761947,-17803.04522,-17795.04681,5.213497939,9, std logFC = 5.213,,broad,STK32A +99,RFX4,100,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000111783,Astro,2.487167051,OPC,0.6013049407,4.136282413,24,Astro/OPC: 4.136,2.23519419,-13159.12695,-13151.12854,3.026513325,9, std logFC = 3.027,,broad,RFX4 diff --git a/panel_design/split/6_top150.csv b/panel_design/split/6_top150.csv new file mode 100644 index 0000000000000000000000000000000000000000..ac6a3f251614d320d374e0a00849a20f0e52f3d4 --- /dev/null +++ b/panel_design/split/6_top150.csv @@ -0,0 +1,151 @@ +Unnamed: 0,symbol,Ranking,Annotation & Reasoning,ensembl_id,cellType.target,mean.target,cellType,mean,ratio,rank_ratio,anno_ratio,logFC,log.p.value,log.FDR,std.logFC,rank_marker,anno_logFC,Unnamed: 17,cellTypeResolution,Gene Symbol +0,BTBD11,1,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000151136,Inhib,2.607133345,OPC,0.3050867244,8.545548319,7,Inhib/OPC: 8.546,2.221299082,-22165.77242,-22155.57679,2.991557876,1, std logFC = 2.992,,broad,BTBD11 +1,ST18,2,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000147488,Oligo,4.651734362,Micro,0.3638996875,12.78301279,11,Oligo/Micro: 12.783,4.465531379,-38170.35,-38160.15437,4.392440029,1, std logFC = 4.392,,broad,ST18 +2,AC004852.2,3,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000278254,OPC,3.198955525,Inhib,0.06788469054,47.12337199,1,OPC/Inhib: 47.123,3.162152196,-34123.87078,-34113.67514,8.5255685,1, std logFC = 8.526,,broad,AC004852.2 +3,OBI1-AS1,4,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000234377,Astro,3.758269422,Oligo,0.3138484541,11.97479029,2,Astro/Oligo: 11.975,3.571921082,-22878.94594,-22868.75031,4.389697553,1, std logFC = 4.39,,broad,OBI1-AS1 +4,ITIH5,5,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000123243,EndoMural,3.034600494,Micro,0.0843586809,35.972593,2,EndoMural/Micro: 35.973,2.979076489,-24405.49833,-24395.3027,6.140134848,1, std logFC = 6.14,,broad,ITIH5 +5,DOCK8,6,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000107099,Micro,3.89732526,EndoMural,0.2363190559,16.4917943,19,Micro/EndoMural: 16.492,3.849979809,-33550.52388,-33540.32824,9.123545355,1, std logFC = 9.124,,broad,DOCK8 +6,BTBD11,7,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000151136,Inhib,2.607133345,Excit_L2/3,0.4583811315,5.687697783,4,Inhib/Excit_L2/3: 5.688,2.232219442,-21879.15743,-21868.96179,3.009130469,1, std logFC = 3.009,,layer,BTBD11 +7,ST18,8,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000147488,Oligo,4.651734362,Micro,0.3638996875,12.78301279,11,Oligo/Micro: 12.783,4.498617988,-37842.74672,-37832.55109,4.45769983,1, std logFC = 4.458,,layer,ST18 +8,AC004852.2,9,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000278254,OPC,3.198955525,Inhib,0.06788469054,47.12337199,1,OPC/Inhib: 47.123,3.161932798,-33191.99065,-33181.79502,8.447310226,1, std logFC = 8.447,,layer,AC004852.2 +9,MAP1B,10,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000131711,Excit_L3/4/5,5.481322199,Excit_L4,4.859234787,1.128021682,25,Excit_L3/4/5/Excit_L4: 1.128,2.357513634,-3728.573791,-3718.378156,1.697613701,1, std logFC = 1.698,,layer,MAP1B +10,CBLN2,11,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000141668,Excit_L3,2.326846695,Excit_L5/6,1.692531181,1.37477331,21,Excit_L3/Excit_L5/6: 1.375,1.884852238,-12389.93168,-12379.73605,1.969356146,1, std logFC = 1.969,,layer,CBLN2 +11,OBI1-AS1,12,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000234377,Astro,3.758269422,Oligo,0.3138484541,11.97479029,2,Astro/Oligo: 11.975,3.59279821,-24845.60054,-24835.4049,4.724479205,1, std logFC = 4.724,,layer,OBI1-AS1 +12,ITIH5,13,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000123243,EndoMural,3.034600494,Excit_L3/4/5,0.1202223609,25.24156465,3,EndoMural/Excit_L3/4/5: 25.242,2.982326922,-24223.80788,-24213.61225,6.170504958,1, std logFC = 6.171,,layer,ITIH5 +13,DOCK8,14,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000107099,Micro,3.89732526,EndoMural,0.2363190559,16.4917943,17,Micro/EndoMural: 16.492,3.849829499,-32837.07734,-32826.8817,9.082881361,1, std logFC = 9.083,,layer,DOCK8 +14,MCTP2,15,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000140563,Excit_L6,1.145245232,EndoMural,0.1418083509,8.076006977,2,Excit_L6/EndoMural: 8.076,1.102705535,-6974.182921,-6963.987287,3.03953067,1, std logFC = 3.04,,layer,MCTP2 +15,THEMIS,16,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000172673,Excit_L5/6,1.180947014,Excit_L5,0.4727839547,2.497857641,2,Excit_L5/6/Excit_L5: 2.498,1.046802894,-4183.521725,-4173.326091,1.965745525,1, std logFC = 1.966,,layer,THEMIS +16,AP003066.1,17,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000254587,Excit_L5,0.9709158406,Excit_L4,0.291616737,3.329424266,1,Excit_L5/Excit_L4: 3.329,0.9097670434,-7118.396732,-7108.201098,2.6322311,1, std logFC = 2.632,,layer,AP003066.1 +17,GAD2,18,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000136750,Inhib,2.326517424,Oligo,0.1658070258,14.03147673,3,Inhib/Oligo: 14.031,2.115583238,-20977.67585,-20968.17336,2.875964071,2, std logFC = 2.876,,broad,GAD2 +18,PDGFRA,19,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000134853,OPC,2.708879833,EndoMural,0.1486911615,18.21816311,4,OPC/EndoMural: 18.218,2.660844387,-24926.02107,-24916.51858,6.623062703,2, std logFC = 6.623,,broad,PDGFRA +19,CABP1,20,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000157782,Excit,2.510583463,Oligo,0.504915941,4.972280055,21,Excit/Oligo: 4.972,1.913232828,-17212.32586,-17202.82338,1.918615179,2, std logFC = 1.919,,broad,CABP1 +20,ADGRV1,21,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164199,Astro,4.473246084,Excit,0.5158270259,8.671988591,6,Astro/Excit: 8.672,3.978323448,-20881.17828,-20871.67579,4.110730183,2, std logFC = 4.111,,broad,ADGRV1 +21,EBF1,22,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164330,EndoMural,3.366858494,Inhib,0.1538390138,21.88559593,7,EndoMural/Inhib: 21.886,3.28545377,-19807.44179,-19797.9393,5.282737171,2, std logFC = 5.283,,broad,EBF1 +22,APBB1IP,23,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000077420,Micro,3.82802585,EndoMural,0.1379134818,27.75671966,12,Micro/EndoMural: 27.757,3.785317824,-33045.61491,-33036.11242,9.006461122,2, std logFC = 9.006,,broad,APBB1IP +23,GAD2,24,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000136750,Inhib,2.326517424,Excit_L2/3,0.6869905244,3.386534954,9,Inhib/Excit_L2/3: 3.387,2.117471009,-21035.58962,-21026.08713,2.924786644,2, std logFC = 2.925,,layer,GAD2 +24,PDGFRA,25,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000134853,OPC,2.708879833,EndoMural,0.1486911615,18.21816311,4,OPC/EndoMural: 18.218,2.661366083,-24639.22108,-24629.7186,6.636112056,2, std logFC = 6.636,,layer,PDGFRA +25,CALM1,26,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198668,Excit_L3/4/5,5.74028179,Excit_L4,4.987590797,1.15091274,15,Excit_L3/4/5/Excit_L4: 1.151,2.354909866,-3489.442816,-3479.940328,1.638514659,2, std logFC = 1.639,,layer,CALM1 +26,CUX2,27,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000111249,Excit_L3,2.400736547,Inhib,1.535578791,1.563408248,7,Excit_L3/Inhib: 1.563,1.969430629,-12347.48171,-12337.97923,1.965153047,2, std logFC = 1.965,,layer,CUX2 +27,ADGRV1,28,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164199,Astro,4.473246084,Excit_L3,0.5998035463,7.457852011,6,Astro/Excit_L3: 7.458,3.997336738,-20967.08141,-20957.57892,4.167294033,2, std logFC = 4.167,,layer,ADGRV1 +28,EBF1,29,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164330,EndoMural,3.366858494,Inhib,0.1538390138,21.88559593,5,EndoMural/Inhib: 21.886,3.297611891,-21376.38612,-21366.88363,5.626266372,2, std logFC = 5.626,,layer,EBF1 +29,APBB1IP,30,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000077420,Micro,3.82802585,EndoMural,0.1379134818,27.75671966,11,Micro/EndoMural: 27.757,3.786556774,-32457.1598,-32447.65732,8.992548136,2, std logFC = 8.993,,layer,APBB1IP +30,AC099517.1,31,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000287176,Excit_L5/6,1.096716549,Excit_L5,0.7892758353,1.389522522,22,Excit_L5/6/Excit_L5: 1.39,0.9844434124,-4108.21197,-4098.709483,1.94656857,2, std logFC = 1.947,,layer,AC099517.1 +31,AC073091.3,32,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000287172,Excit_L5,2.799017012,Excit_L5/6,1.732461721,1.615629932,14,Excit_L5/Excit_L5/6: 1.616,2.240268387,-5465.523097,-5456.02061,2.270115933,2, std logFC = 2.27,,layer,AC073091.3 +32,MOBP,33,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000168314,Oligo,3.538881555,Micro,0.3821774358,9.259786749,19,Oligo/Micro: 9.26,3.2201278,-27951.86856,-27942.77154,3.37455489,3, std logFC = 3.375,,broad,MOBP +33,MEGF11,34,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000157890,OPC,3.351998122,Inhib,0.2164735628,15.48456116,5,OPC/Inhib: 15.485,3.22097198,-24488.41936,-24479.32234,6.535601574,3, std logFC = 6.536,,broad,MEGF11 +34,ADAM28,35,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000042980,Micro,3.005809501,Inhib,0.1116458658,26.92271209,14,Micro/Inhib: 26.923,2.953040163,-26207.24442,-26198.1474,7.470789811,3, std logFC = 7.471,,broad,ADAM28 +35,GAD1,36,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000128683,Inhib,2.370257819,OPC,0.9781879376,2.423110864,20,Inhib/OPC: 2.423,2.134891009,-20843.6985,-20834.60148,2.905628895,3, std logFC = 2.906,,layer,GAD1 +36,MOBP,37,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000168314,Oligo,3.538881555,Excit_L2/3,0.429038715,8.248396779,19,Oligo/Excit_L2/3: 8.248,3.264762216,-28589.15327,-28580.05624,3.498833224,3, std logFC = 3.499,,layer,MOBP +37,MEGF11,38,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000157890,OPC,3.351998122,Excit_L6,0.4393208706,7.6299542,8,OPC/Excit_L6: 7.63,3.221269673,-24076.45133,-24067.35431,6.521045022,3, std logFC = 6.521,,layer,MEGF11 +38,TUBA1B,39,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000123416,Excit_L3/4/5,3.856794784,Excit_L4,3.259121796,1.183384674,10,Excit_L3/4/5/Excit_L4: 1.183,2.152929616,-3376.611792,-3367.51477,1.610062965,3, std logFC = 1.61,,layer,TUBA1B +39,TSHZ2,40,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000182463,Excit_L4,2.513501671,Excit_L5,1.815381111,1.384558678,1,Excit_L4/Excit_L5: 1.385,2.041678543,-3525.994817,-3516.897794,1.825827726,3, std logFC = 1.826,,layer,TSHZ2 +40,AL137139.2,41,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000286757,Astro,2.750399175,EndoMural,0.7166476389,3.837868188,20,Astro/EndoMural: 3.838,2.595534945,-17585.73933,-17576.64231,3.686137516,3, std logFC = 3.686,,layer,AL137139.2 +41,EPAS1,42,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000116016,EndoMural,3.286727061,OPC,0.583780088,5.630077368,24,EndoMural/OPC: 5.63,3.13899098,-18569.36355,-18560.26653,5.094193063,3, std logFC = 5.094,,layer,EPAS1 +42,ADAM28,43,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000042980,Micro,3.005809501,Inhib,0.1116458658,26.92271209,13,Micro/Inhib: 26.923,2.953090913,-25692.73878,-25683.64175,7.436880268,3, std logFC = 7.437,,layer,ADAM28 +43,LINC00343,44,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000226620,Excit_L5/6,0.6838777434,Excit_L4,0.1966485482,3.477664848,1,Excit_L5/6/Excit_L4: 3.478,0.612425043,-3815.624154,-3806.527131,1.870723949,3, std logFC = 1.871,,layer,LINC00343 +44,AL033539.2,45,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000286954,Excit_L5,0.5831326126,Excit_L4,0.2623003828,2.223148157,4,Excit_L5/Excit_L4: 2.223,0.5387135463,-5367.091019,-5357.993996,2.247459805,3, std logFC = 2.247,,layer,AL033539.2 +45,GRIP2,46,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000144596,Inhib,1.454320477,EndoMural,0.1302827841,11.16279857,6,Inhib/EndoMural: 11.163,1.294597378,-18824.6624,-18815.85306,2.666933752,4, std logFC = 2.667,,broad,GRIP2 +46,BX284613.2,47,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000231424,OPC,3.206382317,EndoMural,0.496363375,6.459747995,13,OPC/EndoMural: 6.46,3.113238963,-23593.02996,-23584.22062,6.357212581,4, std logFC = 6.357,,broad,BX284613.2 +47,LINC00299,48,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000236790,Astro,3.197232057,Excit,0.4970508863,6.432403895,11,Astro/Excit: 6.432,2.843051279,-15690.46465,-15681.65531,3.386312678,4, std logFC = 3.386,,broad,LINC00299 +48,FLT1,49,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000102755,EndoMural,3.250079224,Micro,0.2425572587,13.39922475,12,EndoMural/Micro: 13.399,3.128544555,-15945.16946,-15936.36012,4.563792751,4, std logFC = 4.564,,broad,FLT1 +49,TBXAS1,50,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000059377,Micro,2.968864785,Astro,0.1011493705,29.35129273,10,Micro/Astro: 29.351,2.920644873,-24296.42317,-24287.61383,7.054872707,4, std logFC = 7.055,,broad,TBXAS1 +50,ZNF385D,51,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000151789,Inhib,3.711723082,Excit_L2/3,1.636083675,2.268663357,23,Inhib/Excit_L2/3: 2.269,3.094519038,-19109.90017,-19101.09083,2.732838004,4, std logFC = 2.733,,layer,ZNF385D +51,VCAN,52,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000038427,OPC,4.239971898,Astro,0.6973526945,6.080096817,14,OPC/Astro: 6.08,4.093962541,-23078.97859,-23070.16925,6.317945452,4, std logFC = 6.318,,layer,VCAN +52,STMN2,53,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000104435,Excit_L3/4/5,3.558073538,Excit_L4,3.151266501,1.129093187,24,Excit_L3/4/5/Excit_L4: 1.129,2.066072579,-2987.657537,-2978.848197,1.508844291,4, std logFC = 1.509,,layer,STMN2 +53,FLT1,54,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000102755,EndoMural,3.250079224,Micro,0.2425572587,13.39922475,10,EndoMural/Micro: 13.399,3.135745883,-15914.36183,-15905.55249,4.590363107,4, std logFC = 4.59,,layer,FLT1 +54,TBXAS1,55,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000059377,Micro,2.968864785,Astro,0.1011493705,29.35129273,9,Micro/Astro: 29.351,2.920646171,-23800.559,-23791.74966,7.016004295,4, std logFC = 7.016,,layer,TBXAS1 +55,AC019211.1,56,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000239498,Excit_L5/6,2.768667305,Excit_L3,1.951978314,1.418390402,19,Excit_L5/6/Excit_L3: 1.418,1.936861229,-3285.072502,-3276.263162,1.72699297,4, std logFC = 1.727,,layer,AC019211.1 +56,TLL1,57,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000038295,Excit_L5,1.566350741,Excit_L5/6,0.7142612438,2.192966165,5,Excit_L5/Excit_L5/6: 2.193,1.413459313,-5326.778106,-5317.968765,2.238138652,4, std logFC = 2.238,,layer,TLL1 +57,TF,58,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000091513,Oligo,3.281925193,Astro,0.4361364794,7.524995838,22,Oligo/Astro: 7.525,2.974140605,-25258.08742,-25249.50122,3.120593098,5, std logFC = 3.121,,broad,TF +58,VCAN,59,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000038427,OPC,4.239971898,Astro,0.6973526945,6.080096817,15,OPC/Astro: 6.08,4.093854266,-23366.01208,-23357.42589,6.312093879,5, std logFC = 6.312,,broad,VCAN +59,PRDM16,60,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000142611,Astro,1.965483537,EndoMural,0.1415356302,13.88684626,1,Astro/EndoMural: 13.887,1.890689945,-15153.31748,-15144.73129,3.31059695,5, std logFC = 3.311,,broad,PRDM16 +60,COBLL1,61,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000082438,EndoMural,3.535001711,Excit,0.3801767269,9.29831171,17,EndoMural/Excit: 9.298,3.273943496,-15446.89134,-15438.30514,4.470288074,5, std logFC = 4.47,,broad,COBLL1 +61,CSF2RA,62,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198223,Micro,2.489448328,EndoMural,0.03987363654,62.43344085,5,Micro/EndoMural: 62.433,2.464231696,-23546.26297,-23537.67677,6.892680072,5, std logFC = 6.893,,broad,CSF2RA +62,GRIP2,63,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000144596,Inhib,1.454320477,Excit_L2/3,0.41529399,3.501905907,8,Inhib/Excit_L2/3: 3.502,1.297473899,-18670.68231,-18662.09611,2.689107384,5, std logFC = 2.689,,layer,GRIP2 +63,TF,64,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000091513,Oligo,3.281925193,Astro,0.4361364794,7.524995838,21,Oligo/Astro: 7.525,3.031333133,-26430.26489,-26421.6787,3.287157931,5, std logFC = 3.287,,layer,TF +64,BX284613.2,65,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000231424,OPC,3.206382317,EndoMural,0.496363375,6.459747995,12,OPC/EndoMural: 6.46,3.111097484,-22884.31923,-22875.73304,6.278424716,5, std logFC = 6.278,,layer,BX284613.2 +65,CALM3,66,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000160014,Excit_L3/4/5,3.709015615,Excit_L4,3.223571557,1.150591991,16,Excit_L3/4/5/Excit_L4: 1.151,1.985257658,-2909.569992,-2900.983795,1.487876047,5, std logFC = 1.488,,layer,CALM3 +66,AC092957.1,67,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000243620,EndoMural,2.16355825,Astro,0.1154847537,18.73457908,6,EndoMural/Astro: 18.735,2.136118087,-15352.65638,-15344.07018,4.48316734,5, std logFC = 4.483,,layer,AC092957.1 +67,CSF2RA,68,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198223,Micro,2.489448328,Excit_L2/3,0.04203762655,59.21952623,5,Micro/Excit_L2/3: 59.22,2.464479819,-23024.7494,-23016.1632,6.844712531,5, std logFC = 6.845,,layer,CSF2RA +68,LINC02718,69,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000255418,Excit_L6,0.6996451567,EndoMural,0.06321257245,11.06813296,1,Excit_L6/EndoMural: 11.068,0.6548837902,-4351.073982,-4342.487785,2.341138402,5, std logFC = 2.341,,layer,LINC02718 +69,CASC15,70,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000272168,Excit_L5,3.445054998,Excit_L4,2.00686097,1.716638596,13,Excit_L5/Excit_L4: 1.717,2.561174412,-5282.18967,-5273.603473,2.227799686,5, std logFC = 2.228,,layer,CASC15 +70,ENPP2,71,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000136960,Oligo,2.833830413,OPC,0.2844518545,9.962425514,15,Oligo/OPC: 9.962,2.707451245,-24995.76515,-24987.36127,3.096082136,6, std logFC = 3.096,,broad,ENPP2 +71,LHFPL3,72,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000187416,OPC,5.943482667,Inhib,0.8290598316,7.168942988,10,OPC/Inhib: 7.169,5.602913607,-20162.95467,-20154.5508,5.678810335,6, std logFC = 5.679,,broad,LHFPL3 +72,AC092957.1,73,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000243620,EndoMural,2.16355825,Astro,0.1154847537,18.73457908,8,EndoMural/Astro: 18.735,2.134194384,-15379.8881,-15371.48422,4.457692082,6, std logFC = 4.458,,broad,AC092957.1 +73,FYB1,74,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000082074,Micro,2.551032802,EndoMural,0.1481983393,17.2136396,18,Micro/EndoMural: 17.214,2.523305139,-21189.15714,-21180.75326,6.385918489,6, std logFC = 6.386,,broad,FYB1 +74,LHFPL3,75,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000187416,OPC,5.943482667,Inhib,0.8290598316,7.168942988,9,OPC/Inhib: 7.169,5.625539581,-20159.08824,-20150.68437,5.728066973,6, std logFC = 5.728,,layer,LHFPL3 +75,NORAD,76,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000260032,Excit_L3/4/5,3.350699442,Excit_L4,2.841384123,1.17924902,11,Excit_L3/4/5/Excit_L4: 1.179,1.867739455,-2904.971247,-2896.567372,1.486633854,6, std logFC = 1.487,,layer,NORAD +76,AC008574.1,77,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000251293,Excit_L3,0.9575423718,Excit_L2/3,0.4575828704,2.092609741,2,Excit_L3/Excit_L2/3: 2.093,0.8900419455,-10798.82644,-10790.42256,1.809927618,6, std logFC = 1.81,,layer,AC008574.1 +77,PRDM16,78,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000142611,Astro,1.965483537,EndoMural,0.1415356302,13.88684626,1,Astro/EndoMural: 13.887,1.893996413,-14914.85085,-14906.44697,3.304215312,6, std logFC = 3.304,,layer,PRDM16 +78,FYB1,79,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000082074,Micro,2.551032802,EndoMural,0.1481983393,17.2136396,16,Micro/EndoMural: 17.214,2.523447889,-20643.88812,-20635.48424,6.322406089,6, std logFC = 6.322,,layer,FYB1 +79,ADAMTSL1,80,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000178031,Excit_L6,2.377115062,Excit_L5,0.9454280519,2.514326773,6,Excit_L6/Excit_L5: 2.514,2.008588364,-4203.428163,-4195.024288,2.297825729,6, std logFC = 2.298,,layer,ADAMTSL1 +80,ANK1,81,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000029534,Inhib,1.640331625,Excit,0.2162203098,7.586390134,10,Inhib/Excit: 7.586,1.474123431,-16081.11571,-16072.86598,2.399709636,7, std logFC = 2.4,,broad,ANK1 +81,FERMT1,82,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000101311,OPC,1.722814414,EndoMural,0.04082874593,42.1961139,2,OPC/EndoMural: 42.196,1.692500655,-18984.97429,-18976.72456,5.446649042,7, std logFC = 5.447,,broad,FERMT1 +82,MLIP,83,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000146147,Excit,1.646129521,Oligo,0.2236439769,7.360491186,10,Excit/Oligo: 7.36,1.419368353,-12476.19844,-12467.94872,1.560524131,7, std logFC = 1.561,,broad,MLIP +83,GLI3,84,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000106571,Astro,1.974149301,EndoMural,0.407848692,4.840396305,18,Astro/EndoMural: 4.84,1.886689057,-14289.69413,-14281.44441,3.18822336,7, std logFC = 3.188,,broad,GLI3 +84,ATP10A,85,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000206190,EndoMural,2.90391912,Excit,0.1122570077,25.86848855,5,EndoMural/Excit: 25.868,2.803930781,-15174.06071,-15165.81099,4.418962316,7, std logFC = 4.419,,broad,ATP10A +85,ANK1,86,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000029534,Inhib,1.640331625,Excit_L2/3,0.6175970678,2.655989983,14,Inhib/Excit_L2/3: 2.656,1.50843475,-17034.06009,-17025.81037,2.526017253,7, std logFC = 2.526,,layer,ANK1 +86,ENPP2,87,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000136960,Oligo,2.833830413,OPC,0.2844518545,9.962425514,14,Oligo/OPC: 9.962,2.734614411,-25019.02205,-25010.77233,3.150591195,7, std logFC = 3.151,,layer,ENPP2 +87,COL9A1,88,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000112280,OPC,1.788626477,EndoMural,0.09293992573,19.24497425,3,OPC/EndoMural: 19.245,1.762865747,-18536.14412,-18527.8944,5.401756121,7, std logFC = 5.402,,layer,COL9A1 +88,GLI3,89,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000106571,Astro,1.974149301,EndoMural,0.407848692,4.840396305,13,Astro/EndoMural: 4.84,1.886750458,-13995.79917,-13987.54944,3.171460728,7, std logFC = 3.171,,layer,GLI3 +89,ATP10A,90,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000206190,EndoMural,2.90391912,Excit_L6,0.2181010003,13.31456122,11,EndoMural/Excit_L6: 13.315,2.806470362,-14997.12634,-14988.87662,4.415139852,7, std logFC = 4.415,,layer,ATP10A +90,C3,91,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000125730,Micro,2.52206604,Oligo,0.07219728309,34.93297714,7,Micro/Oligo: 34.933,2.485800545,-20240.83407,-20232.58435,6.234341526,7, std logFC = 6.234,,layer,C3 +91,COL9A1,92,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000112280,OPC,1.788626477,EndoMural,0.09293992573,19.24497425,3,OPC/EndoMural: 19.245,1.762132666,-18731.56562,-18723.44942,5.396693614,8, std logFC = 5.397,,broad,COL9A1 +92,CARMN,93,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000249669,EndoMural,1.643565406,Oligo,0.0263563217,62.35943788,1,EndoMural/Oligo: 62.359,1.627501258,-14393.44504,-14385.32884,4.271525569,8, std logFC = 4.272,,broad,CARMN +93,LINC01374,94,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000280560,Micro,2.665601597,Inhib,0.08852780987,30.11033031,9,Micro/Inhib: 30.11,2.614257162,-19845.10532,-19836.98913,6.098132232,8, std logFC = 6.098,,broad,LINC01374 +94,TMEM144,95,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164124,Oligo,3.011743854,Astro,0.5404121759,5.573049588,24,Oligo/Astro: 5.573,2.813875241,-23039.42418,-23031.30798,2.9609775,8, std logFC = 2.961,,layer,TMEM144 +95,FERMT1,96,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000101311,OPC,1.722814414,Excit_L5,0.04741369715,36.33579572,2,OPC/Excit_L5: 36.336,1.693277281,-18530.83724,-18522.72105,5.400689362,8, std logFC = 5.401,,layer,FERMT1 +96,ABCG2,97,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000118777,EndoMural,2.223753949,Micro,0.1416969614,15.69373067,8,EndoMural/Micro: 15.694,2.171291149,-14281.57978,-14273.46359,4.277720977,8, std logFC = 4.278,,layer,ABCG2 +97,AC109466.1,98,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000241956,Excit_L5,2.875408149,Excit_L5/6,1.64483523,1.748143582,11,Excit_L5/Excit_L5/6: 1.748,2.381282963,-4736.240644,-4728.124451,2.098532254,8, std logFC = 2.099,,layer,AC109466.1 +98,STK32A,99,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000169302,OPC,2.019957136,Astro,0.1383572451,14.59957616,6,OPC/Astro: 14.6,1.957761947,-17803.04522,-17795.04681,5.213497939,9, std logFC = 5.213,,broad,STK32A +99,RFX4,100,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000111783,Astro,2.487167051,OPC,0.6013049407,4.136282413,24,Astro/OPC: 4.136,2.23519419,-13159.12695,-13151.12854,3.026513325,9, std logFC = 3.027,,broad,RFX4 +100,ABCG2,101,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000118777,EndoMural,2.223753949,Micro,0.1416969614,15.69373067,11,EndoMural/Micro: 15.694,2.168238435,-14358.17224,-14350.17383,4.264840793,9, std logFC = 4.265,,broad,ABCG2 +101,C3,102,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000125730,Micro,2.52206604,Oligo,0.07219728309,34.93297714,8,Micro/Oligo: 34.933,2.479048886,-19530.91812,-19522.91971,6.030900834,9, std logFC = 6.031,,broad,C3 +102,IGF1,103,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000017427,Inhib,1.207954344,Micro,0.4643309408,2.601494403,15,Inhib/Micro: 2.601,1.085057741,-12337.42096,-12329.42255,2.050834687,9, std logFC = 2.051,,layer,IGF1 +103,STK32A,104,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000169302,OPC,2.019957136,Astro,0.1383572451,14.59957616,5,OPC/Astro: 14.6,1.960969367,-17786.92561,-17778.9272,5.251108072,9, std logFC = 5.251,,layer,STK32A +104,IDS,105,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000010404,Excit_L3/4/5,3.71431134,Excit_L4,3.097842565,1.198999388,7,Excit_L3/4/5/Excit_L4: 1.199,1.822760661,-2652.958629,-2644.960219,1.417233814,9, std logFC = 1.417,,layer,IDS +105,RFX4,106,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000111783,Astro,2.487167051,OPC,0.6013049407,4.136282413,19,Astro/OPC: 4.136,2.235231865,-13002.89052,-12994.89211,3.026793957,9, std logFC = 3.027,,layer,RFX4 +106,CARMN,107,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000249669,EndoMural,1.643565406,Oligo,0.0263563217,62.35943788,1,EndoMural/Oligo: 62.359,1.62914635,-14151.25905,-14143.26064,4.252610704,9, std logFC = 4.253,,layer,CARMN +107,LINC01374,108,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000280560,Micro,2.665601597,Excit_L2/3,0.1209461776,22.03956875,14,Micro/Excit_L2/3: 22.04,2.614088563,-19368.9252,-19360.92679,6.044024163,9, std logFC = 6.044,,layer,LINC01374 +108,KIAA1217,109,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000120549,Excit_L6,4.476844625,Inhib,2.447500224,1.829149833,12,Excit_L6/Inhib: 1.829,3.134164009,-3063.328171,-3055.329761,1.940285622,9, std logFC = 1.94,,layer,KIAA1217 +109,SYNPR,110,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000163630,Inhib,3.120004834,Excit,0.8724556595,3.576118511,23,Inhib/Excit: 3.576,2.348625671,-12051.46778,-12043.57473,1.998522788,10, std logFC = 1.999,,broad,SYNPR +110,SMOC1,111,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198732,OPC,2.695256688,Oligo,0.4193902381,6.426608069,14,OPC/Oligo: 6.427,2.5575356,-12775.37056,-12767.47751,4.206881046,10, std logFC = 4.207,,broad,SMOC1 +111,MECOM,112,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000085276,EndoMural,2.250476203,OPC,0.1016495433,22.1395604,6,EndoMural/OPC: 22.14,2.20961047,-14328.39711,-14320.50406,4.259196264,10, std logFC = 4.259,,broad,MECOM +112,BLNK,113,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000095585,Micro,2.002882461,Oligo,0.02678543501,74.77505818,3,Micro/Oligo: 74.775,1.982263748,-18454.05934,-18446.16629,5.800409249,10, std logFC = 5.8,,broad,BLNK +113,SMOC1,114,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198732,OPC,2.695256688,Oligo,0.4193902381,6.426608069,13,OPC/Oligo: 6.427,2.558973129,-12649.09865,-12641.2056,4.205190442,10, std logFC = 4.205,,layer,SMOC1 +114,CALM2,115,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000143933,Excit_L3/4/5,4.396157921,Excit_L4,3.807952517,1.154467631,14,Excit_L3/4/5/Excit_L4: 1.154,1.848887924,-2643.918919,-2636.025869,1.414693444,10, std logFC = 1.415,,layer,CALM2 +115,MECOM,116,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000085276,EndoMural,2.250476203,OPC,0.1016495433,22.1395604,4,EndoMural/OPC: 22.14,2.210046204,-13992.7995,-13984.90645,4.222040776,10, std logFC = 4.222,,layer,MECOM +116,BLNK,117,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000095585,Micro,2.002882461,Oligo,0.02678543501,74.77505818,3,Micro/Oligo: 74.775,1.982020944,-17972.63512,-17964.74207,5.739397864,10, std logFC = 5.739,,layer,BLNK +117,AC073091.4,118,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000287749,Excit_L5,1.215286255,Excit_L5/6,0.6366974551,1.908734275,8,Excit_L5/Excit_L5/6: 1.909,1.02001771,-4584.970314,-4577.077264,2.061760921,10, std logFC = 2.062,,layer,AC073091.4 +118,MYT1,119,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000196132,OPC,1.908497271,Inhib,0.3464032729,5.509466625,17,OPC/Inhib: 5.509,1.752463202,-12392.57639,-12384.77865,4.128283609,11, std logFC = 4.128,,broad,MYT1 +119,PAMR1,120,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000149090,Astro,2.120835277,Excit,0.200048431,10.60160915,3,Astro/Excit: 10.602,1.952587157,-12481.13615,-12473.33841,2.928504013,11, std logFC = 2.929,,broad,PAMR1 +120,SYNE2,121,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000054654,EndoMural,2.567241961,OPC,0.3409544082,7.529575506,22,EndoMural/OPC: 7.53,2.395636832,-13465.04564,-13457.2479,4.094813686,11, std logFC = 4.095,,broad,SYNE2 +121,IKZF1,122,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000185811,Micro,2.062837557,EndoMural,0.1349670194,15.28401209,22,Micro/EndoMural: 15.284,2.044672516,-18057.65993,-18049.86219,5.71549041,11, std logFC = 5.715,,broad,IKZF1 +122,MYT1,123,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000196132,OPC,1.908497271,Inhib,0.3464032729,5.509466625,16,OPC/Inhib: 5.509,1.755374759,-12304.9398,-12297.14206,4.133464035,11, std logFC = 4.133,,layer,MYT1 +123,LINC01378,124,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000236922,Excit_L3,1.599490081,Excit_L5,1.049665513,1.52380931,8,Excit_L3/Excit_L5: 1.524,1.308563463,-9071.786765,-9063.989026,1.631114364,11, std logFC = 1.631,,layer,LINC01378 +124,COL5A3,125,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000080573,Astro,2.302844157,EndoMural,0.6570010778,3.505084291,22,Astro/EndoMural: 3.505,2.123110144,-12625.76528,-12617.96754,2.971429657,11, std logFC = 2.971,,layer,COL5A3 +125,SYNE2,126,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000054654,EndoMural,2.567241961,OPC,0.3409544082,7.529575506,19,EndoMural/OPC: 7.53,2.39662461,-13427.85074,-13420.053,4.112684037,11, std logFC = 4.113,,layer,SYNE2 +126,IKZF1,127,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000185811,Micro,2.062837557,EndoMural,0.1349670194,15.28401209,19,Micro/EndoMural: 15.284,2.044819961,-17628.70658,-17620.90884,5.664314641,11, std logFC = 5.664,,layer,IKZF1 +127,TRABD2A,128,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000186854,Excit_L5,0.7658593859,Excit_L5/6,0.3389938883,2.25921296,3,Excit_L5/Excit_L5/6: 2.259,0.681590249,-4356.309844,-4348.512105,2.005299079,11, std logFC = 2.005,,layer,TRABD2A +128,SLC12A8,129,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000221955,Excit_L2/3,0.9334811335,Excit_L3,0.6588626127,1.416806957,2,Excit_L2/3/Excit_L3: 1.417,0.586042251,-50.2780032,-42.48026383,1.074640783,11, std logFC = 1.075,,layer,SLC12A8 +129,GRIN3A,130,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198785,Inhib,1.730543079,Excit,0.2999986878,5.76850216,12,Inhib/Excit: 5.769,1.438277757,-11545.03817,-11537.32744,1.946698438,12, std logFC = 1.947,,broad,GRIN3A +130,NTN1,131,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000065320,OPC,1.78926937,EndoMural,0.4206738151,4.253341439,21,OPC/EndoMural: 4.253,1.712181691,-11287.19113,-11279.4804,3.898727563,12, std logFC = 3.899,,broad,NTN1 +131,SYK,132,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000165025,Micro,2.053288275,Inhib,0.09744501314,21.07125043,16,Micro/Inhib: 21.071,2.029754872,-17869.42799,-17861.71726,5.675143288,12, std logFC = 5.675,,broad,SYK +132,GRIN3A,133,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198785,Inhib,1.730543079,Excit_L5,0.7667857832,2.256879452,24,Inhib/Excit_L5: 2.257,1.446197713,-11453.13181,-11445.42109,1.958781122,12, std logFC = 1.959,,layer,GRIN3A +133,CACNG4,134,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000075461,OPC,1.829603104,Inhib,0.3062213481,5.9747732,15,OPC/Inhib: 5.975,1.751404365,-11050.48585,-11042.77512,3.868906114,12, std logFC = 3.869,,layer,CACNG4 +134,LINC02296,135,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000258859,Excit_L3,1.03274461,Excit_L2/3,0.4288175933,2.408354103,1,Excit_L3/Excit_L2/3: 2.408,0.9144674288,-8875.572722,-8867.861994,1.610300614,12, std logFC = 1.61,,layer,LINC02296 +135,PAMR1,136,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000149090,Astro,2.120835277,Excit_L3,0.3288294631,6.449651005,8,Astro/Excit_L3: 6.45,1.956115238,-12358.76127,-12351.05054,2.932072018,12, std logFC = 2.932,,layer,PAMR1 +136,NOTCH3,137,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000074181,EndoMural,1.573248357,Excit_L2/3,0.1117919159,14.07300648,9,EndoMural/Excit_L2/3: 14.073,1.548395314,-12627.72396,-12620.01324,3.956673595,12, std logFC = 3.957,,layer,NOTCH3 +137,SYK,138,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000165025,Micro,2.053288275,Inhib,0.09744501314,21.07125043,15,Micro/Inhib: 21.071,2.029918564,-17422.46491,-17414.75418,5.619268791,12, std logFC = 5.619,,layer,SYK +138,AC007368.1,139,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000255595,Excit_L5/6,2.268679446,Excit_L3,1.576194201,1.439340054,16,Excit_L5/6/Excit_L3: 1.439,1.545775475,-2669.146392,-2661.435664,1.547489937,12, std logFC = 1.547,,layer,AC007368.1 +139,COL12A1,140,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000111799,Excit_L5,1.372629483,Excit_L5/6,0.5188828551,2.645355246,2,Excit_L5/Excit_L5/6: 2.645,1.07218827,-3537.679177,-3529.968449,1.792978663,12, std logFC = 1.793,,layer,COL12A1 +140,KIT,141,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000157404,Inhib,1.559251717,OPC,0.192720352,8.090747556,8,Inhib/OPC: 8.091,1.246343794,-10240.0014,-10232.37071,1.81090138,13, std logFC = 1.811,,broad,KIT +141,CACNG4,142,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000075461,OPC,1.829603104,Inhib,0.3062213481,5.9747732,16,OPC/Inhib: 5.975,1.740927111,-10839.80173,-10832.17104,3.804545954,13, std logFC = 3.805,,broad,CACNG4 +142,SLC25A18,143,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000182902,Astro,1.878715421,Oligo,0.2333016133,8.052732229,8,Astro/Oligo: 8.053,1.738395843,-11293.23757,-11285.60688,2.754404576,13, std logFC = 2.754,,broad,SLC25A18 +143,ITGA1,144,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000213949,EndoMural,1.759660119,Inhib,0.09437121122,18.6461538,9,EndoMural/Inhib: 18.646,1.695886668,-12584.89579,-12577.26511,3.925571863,13, std logFC = 3.926,,broad,ITGA1 +144,NTN1,145,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000065320,OPC,1.78926937,EndoMural,0.4206738151,4.253341439,18,OPC/EndoMural: 4.253,1.710917109,-10986.16239,-10978.53171,3.855189853,13, std logFC = 3.855,,layer,NTN1 +145,ENC1,146,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000171617,Excit_L3/4/5,3.324038993,Excit_L3,2.711415538,1.225942297,5,Excit_L3/4/5/Excit_L3: 1.226,1.964480545,-2462.002024,-2454.371338,1.362750654,13, std logFC = 1.363,,layer,ENC1 +146,SLC25A18,147,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000182902,Astro,1.878715421,Oligo,0.2333016133,8.052732229,5,Astro/Oligo: 8.053,1.764813824,-12083.41538,-12075.7847,2.891334822,13, std logFC = 2.891,,layer,SLC25A18 +147,CLDN5,148,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000184113,EndoMural,1.744698836,Oligo,0.05984230862,29.15493865,2,EndoMural/Oligo: 29.155,1.702327822,-12486.89432,-12479.26363,3.929057898,13, std logFC = 3.929,,layer,CLDN5 +148,DPP4,149,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000197635,Excit_L6,0.6591071977,Excit_L4,0.1867582532,3.529199841,4,Excit_L6/Excit_L4: 3.529,0.5823384537,-2739.020873,-2731.390187,1.82897994,13, std logFC = 1.829,,layer,DPP4 +149,SAMD5,150,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000203727,Inhib,1.963588183,Excit,0.371944217,5.279254503,15,Inhib/Excit: 5.279,1.510233132,-10233.80262,-10226.24604,1.810247485,14, std logFC = 1.81,,broad,SAMD5 diff --git a/panel_design/split/6_top50.csv b/panel_design/split/6_top50.csv new file mode 100644 index 0000000000000000000000000000000000000000..860744262b0fca072a53b462d1768e54c416dde4 --- /dev/null +++ b/panel_design/split/6_top50.csv @@ -0,0 +1,51 @@ +Unnamed: 0,symbol,Ranking,Annotation & Reasoning,ensembl_id,cellType.target,mean.target,cellType,mean,ratio,rank_ratio,anno_ratio,logFC,log.p.value,log.FDR,std.logFC,rank_marker,anno_logFC,Unnamed: 17,cellTypeResolution,Gene Symbol +0,BTBD11,1,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000151136,Inhib,2.607133345,OPC,0.3050867244,8.545548319,7,Inhib/OPC: 8.546,2.221299082,-22165.77242,-22155.57679,2.991557876,1, std logFC = 2.992,,broad,BTBD11 +1,ST18,2,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000147488,Oligo,4.651734362,Micro,0.3638996875,12.78301279,11,Oligo/Micro: 12.783,4.465531379,-38170.35,-38160.15437,4.392440029,1, std logFC = 4.392,,broad,ST18 +2,AC004852.2,3,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000278254,OPC,3.198955525,Inhib,0.06788469054,47.12337199,1,OPC/Inhib: 47.123,3.162152196,-34123.87078,-34113.67514,8.5255685,1, std logFC = 8.526,,broad,AC004852.2 +3,OBI1-AS1,4,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000234377,Astro,3.758269422,Oligo,0.3138484541,11.97479029,2,Astro/Oligo: 11.975,3.571921082,-22878.94594,-22868.75031,4.389697553,1, std logFC = 4.39,,broad,OBI1-AS1 +4,ITIH5,5,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000123243,EndoMural,3.034600494,Micro,0.0843586809,35.972593,2,EndoMural/Micro: 35.973,2.979076489,-24405.49833,-24395.3027,6.140134848,1, std logFC = 6.14,,broad,ITIH5 +5,DOCK8,6,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000107099,Micro,3.89732526,EndoMural,0.2363190559,16.4917943,19,Micro/EndoMural: 16.492,3.849979809,-33550.52388,-33540.32824,9.123545355,1, std logFC = 9.124,,broad,DOCK8 +6,BTBD11,7,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000151136,Inhib,2.607133345,Excit_L2/3,0.4583811315,5.687697783,4,Inhib/Excit_L2/3: 5.688,2.232219442,-21879.15743,-21868.96179,3.009130469,1, std logFC = 3.009,,layer,BTBD11 +7,ST18,8,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000147488,Oligo,4.651734362,Micro,0.3638996875,12.78301279,11,Oligo/Micro: 12.783,4.498617988,-37842.74672,-37832.55109,4.45769983,1, std logFC = 4.458,,layer,ST18 +8,AC004852.2,9,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000278254,OPC,3.198955525,Inhib,0.06788469054,47.12337199,1,OPC/Inhib: 47.123,3.161932798,-33191.99065,-33181.79502,8.447310226,1, std logFC = 8.447,,layer,AC004852.2 +9,MAP1B,10,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000131711,Excit_L3/4/5,5.481322199,Excit_L4,4.859234787,1.128021682,25,Excit_L3/4/5/Excit_L4: 1.128,2.357513634,-3728.573791,-3718.378156,1.697613701,1, std logFC = 1.698,,layer,MAP1B +10,CBLN2,11,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000141668,Excit_L3,2.326846695,Excit_L5/6,1.692531181,1.37477331,21,Excit_L3/Excit_L5/6: 1.375,1.884852238,-12389.93168,-12379.73605,1.969356146,1, std logFC = 1.969,,layer,CBLN2 +11,OBI1-AS1,12,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000234377,Astro,3.758269422,Oligo,0.3138484541,11.97479029,2,Astro/Oligo: 11.975,3.59279821,-24845.60054,-24835.4049,4.724479205,1, std logFC = 4.724,,layer,OBI1-AS1 +12,ITIH5,13,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000123243,EndoMural,3.034600494,Excit_L3/4/5,0.1202223609,25.24156465,3,EndoMural/Excit_L3/4/5: 25.242,2.982326922,-24223.80788,-24213.61225,6.170504958,1, std logFC = 6.171,,layer,ITIH5 +13,DOCK8,14,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000107099,Micro,3.89732526,EndoMural,0.2363190559,16.4917943,17,Micro/EndoMural: 16.492,3.849829499,-32837.07734,-32826.8817,9.082881361,1, std logFC = 9.083,,layer,DOCK8 +14,MCTP2,15,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000140563,Excit_L6,1.145245232,EndoMural,0.1418083509,8.076006977,2,Excit_L6/EndoMural: 8.076,1.102705535,-6974.182921,-6963.987287,3.03953067,1, std logFC = 3.04,,layer,MCTP2 +15,THEMIS,16,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000172673,Excit_L5/6,1.180947014,Excit_L5,0.4727839547,2.497857641,2,Excit_L5/6/Excit_L5: 2.498,1.046802894,-4183.521725,-4173.326091,1.965745525,1, std logFC = 1.966,,layer,THEMIS +16,AP003066.1,17,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000254587,Excit_L5,0.9709158406,Excit_L4,0.291616737,3.329424266,1,Excit_L5/Excit_L4: 3.329,0.9097670434,-7118.396732,-7108.201098,2.6322311,1, std logFC = 2.632,,layer,AP003066.1 +17,GAD2,18,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000136750,Inhib,2.326517424,Oligo,0.1658070258,14.03147673,3,Inhib/Oligo: 14.031,2.115583238,-20977.67585,-20968.17336,2.875964071,2, std logFC = 2.876,,broad,GAD2 +18,PDGFRA,19,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000134853,OPC,2.708879833,EndoMural,0.1486911615,18.21816311,4,OPC/EndoMural: 18.218,2.660844387,-24926.02107,-24916.51858,6.623062703,2, std logFC = 6.623,,broad,PDGFRA +19,CABP1,20,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000157782,Excit,2.510583463,Oligo,0.504915941,4.972280055,21,Excit/Oligo: 4.972,1.913232828,-17212.32586,-17202.82338,1.918615179,2, std logFC = 1.919,,broad,CABP1 +20,ADGRV1,21,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164199,Astro,4.473246084,Excit,0.5158270259,8.671988591,6,Astro/Excit: 8.672,3.978323448,-20881.17828,-20871.67579,4.110730183,2, std logFC = 4.111,,broad,ADGRV1 +21,EBF1,22,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164330,EndoMural,3.366858494,Inhib,0.1538390138,21.88559593,7,EndoMural/Inhib: 21.886,3.28545377,-19807.44179,-19797.9393,5.282737171,2, std logFC = 5.283,,broad,EBF1 +22,APBB1IP,23,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000077420,Micro,3.82802585,EndoMural,0.1379134818,27.75671966,12,Micro/EndoMural: 27.757,3.785317824,-33045.61491,-33036.11242,9.006461122,2, std logFC = 9.006,,broad,APBB1IP +23,GAD2,24,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000136750,Inhib,2.326517424,Excit_L2/3,0.6869905244,3.386534954,9,Inhib/Excit_L2/3: 3.387,2.117471009,-21035.58962,-21026.08713,2.924786644,2, std logFC = 2.925,,layer,GAD2 +24,PDGFRA,25,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000134853,OPC,2.708879833,EndoMural,0.1486911615,18.21816311,4,OPC/EndoMural: 18.218,2.661366083,-24639.22108,-24629.7186,6.636112056,2, std logFC = 6.636,,layer,PDGFRA +25,CALM1,26,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000198668,Excit_L3/4/5,5.74028179,Excit_L4,4.987590797,1.15091274,15,Excit_L3/4/5/Excit_L4: 1.151,2.354909866,-3489.442816,-3479.940328,1.638514659,2, std logFC = 1.639,,layer,CALM1 +26,CUX2,27,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000111249,Excit_L3,2.400736547,Inhib,1.535578791,1.563408248,7,Excit_L3/Inhib: 1.563,1.969430629,-12347.48171,-12337.97923,1.965153047,2, std logFC = 1.965,,layer,CUX2 +27,ADGRV1,28,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164199,Astro,4.473246084,Excit_L3,0.5998035463,7.457852011,6,Astro/Excit_L3: 7.458,3.997336738,-20967.08141,-20957.57892,4.167294033,2, std logFC = 4.167,,layer,ADGRV1 +28,EBF1,29,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000164330,EndoMural,3.366858494,Inhib,0.1538390138,21.88559593,5,EndoMural/Inhib: 21.886,3.297611891,-21376.38612,-21366.88363,5.626266372,2, std logFC = 5.626,,layer,EBF1 +29,APBB1IP,30,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000077420,Micro,3.82802585,EndoMural,0.1379134818,27.75671966,11,Micro/EndoMural: 27.757,3.786556774,-32457.1598,-32447.65732,8.992548136,2, std logFC = 8.993,,layer,APBB1IP +30,AC099517.1,31,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000287176,Excit_L5/6,1.096716549,Excit_L5,0.7892758353,1.389522522,22,Excit_L5/6/Excit_L5: 1.39,0.9844434124,-4108.21197,-4098.709483,1.94656857,2, std logFC = 1.947,,layer,AC099517.1 +31,AC073091.3,32,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000287172,Excit_L5,2.799017012,Excit_L5/6,1.732461721,1.615629932,14,Excit_L5/Excit_L5/6: 1.616,2.240268387,-5465.523097,-5456.02061,2.270115933,2, std logFC = 2.27,,layer,AC073091.3 +32,MOBP,33,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000168314,Oligo,3.538881555,Micro,0.3821774358,9.259786749,19,Oligo/Micro: 9.26,3.2201278,-27951.86856,-27942.77154,3.37455489,3, std logFC = 3.375,,broad,MOBP +33,MEGF11,34,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000157890,OPC,3.351998122,Inhib,0.2164735628,15.48456116,5,OPC/Inhib: 15.485,3.22097198,-24488.41936,-24479.32234,6.535601574,3, std logFC = 6.536,,broad,MEGF11 +34,ADAM28,35,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000042980,Micro,3.005809501,Inhib,0.1116458658,26.92271209,14,Micro/Inhib: 26.923,2.953040163,-26207.24442,-26198.1474,7.470789811,3, std logFC = 7.471,,broad,ADAM28 +35,GAD1,36,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000128683,Inhib,2.370257819,OPC,0.9781879376,2.423110864,20,Inhib/OPC: 2.423,2.134891009,-20843.6985,-20834.60148,2.905628895,3, std logFC = 2.906,,layer,GAD1 +36,MOBP,37,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000168314,Oligo,3.538881555,Excit_L2/3,0.429038715,8.248396779,19,Oligo/Excit_L2/3: 8.248,3.264762216,-28589.15327,-28580.05624,3.498833224,3, std logFC = 3.499,,layer,MOBP +37,MEGF11,38,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000157890,OPC,3.351998122,Excit_L6,0.4393208706,7.6299542,8,OPC/Excit_L6: 7.63,3.221269673,-24076.45133,-24067.35431,6.521045022,3, std logFC = 6.521,,layer,MEGF11 +38,TUBA1B,39,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000123416,Excit_L3/4/5,3.856794784,Excit_L4,3.259121796,1.183384674,10,Excit_L3/4/5/Excit_L4: 1.183,2.152929616,-3376.611792,-3367.51477,1.610062965,3, std logFC = 1.61,,layer,TUBA1B +39,TSHZ2,40,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000182463,Excit_L4,2.513501671,Excit_L5,1.815381111,1.384558678,1,Excit_L4/Excit_L5: 1.385,2.041678543,-3525.994817,-3516.897794,1.825827726,3, std logFC = 1.826,,layer,TSHZ2 +40,AL137139.2,41,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000286757,Astro,2.750399175,EndoMural,0.7166476389,3.837868188,20,Astro/EndoMural: 3.838,2.595534945,-17585.73933,-17576.64231,3.686137516,3, std logFC = 3.686,,layer,AL137139.2 +41,EPAS1,42,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000116016,EndoMural,3.286727061,OPC,0.583780088,5.630077368,24,EndoMural/OPC: 5.63,3.13899098,-18569.36355,-18560.26653,5.094193063,3, std logFC = 5.094,,layer,EPAS1 +42,ADAM28,43,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000042980,Micro,3.005809501,Inhib,0.1116458658,26.92271209,13,Micro/Inhib: 26.923,2.953090913,-25692.73878,-25683.64175,7.436880268,3, std logFC = 7.437,,layer,ADAM28 +43,LINC00343,44,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000226620,Excit_L5/6,0.6838777434,Excit_L4,0.1966485482,3.477664848,1,Excit_L5/6/Excit_L4: 3.478,0.612425043,-3815.624154,-3806.527131,1.870723949,3, std logFC = 1.871,,layer,LINC00343 +44,AL033539.2,45,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000286954,Excit_L5,0.5831326126,Excit_L4,0.2623003828,2.223148157,4,Excit_L5/Excit_L4: 2.223,0.5387135463,-5367.091019,-5357.993996,2.247459805,3, std logFC = 2.247,,layer,AL033539.2 +45,GRIP2,46,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000144596,Inhib,1.454320477,EndoMural,0.1302827841,11.16279857,6,Inhib/EndoMural: 11.163,1.294597378,-18824.6624,-18815.85306,2.666933752,4, std logFC = 2.667,,broad,GRIP2 +46,BX284613.2,47,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000231424,OPC,3.206382317,EndoMural,0.496363375,6.459747995,13,OPC/EndoMural: 6.46,3.113238963,-23593.02996,-23584.22062,6.357212581,4, std logFC = 6.357,,broad,BX284613.2 +47,LINC00299,48,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000236790,Astro,3.197232057,Excit,0.4970508863,6.432403895,11,Astro/Excit: 6.432,2.843051279,-15690.46465,-15681.65531,3.386312678,4, std logFC = 3.386,,broad,LINC00299 +48,FLT1,49,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000102755,EndoMural,3.250079224,Micro,0.2425572587,13.39922475,12,EndoMural/Micro: 13.399,3.128544555,-15945.16946,-15936.36012,4.563792751,4, std logFC = 4.564,,broad,FLT1 +49,TBXAS1,50,"From literature https://www.science.org/doi/10.1126/science.adh1938 , I didn't use the scdata because I think it can be very noisy",ENSG00000059377,Micro,2.968864785,Astro,0.1011493705,29.35129273,10,Micro/Astro: 29.351,2.920644873,-24296.42317,-24287.61383,7.054872707,4, std logFC = 7.055,,broad,TBXAS1 diff --git a/panel_design/split/7_top100.csv b/panel_design/split/7_top100.csv new file mode 100644 index 0000000000000000000000000000000000000000..985b876beec0947dd74dbf70a8dd790298fbac9d --- /dev/null +++ b/panel_design/split/7_top100.csv @@ -0,0 +1,102 @@ +Unnamed: 0,gene_name,annotation,top_50,top_100,top_150,Gene Symbol +0,ADGRV1,DE in astrocyte in reference dataset,1,1,1,ADGRV1 +1,SLC1A3,astrocyte marker literature,1,1,1,SLC1A3 +2,SLC1A2,astrocyte marker literature,1,1,1,SLC1A2 +3,CDH20,"DE in Oligo, OPC, astrocyte in reference dataset",1,1,1,CDH20 +4,PTPRZ1,DE in OPC and astrocytesin reference dataset,1,1,1,PTPRZ1 +5,ST18,DE in Oligodendrocyte in reference dataset,1,1,1,ST18 +6,MBP,Oligodendrocyte marker / gene linked with methylation,1,1,1,MBP +7,PTGDS,oligodendrocyte subtype marker in literature,1,1,1,PTGDS +8,SST,marker of sstGABAergic cortinal interneuron,1,1,1,SST +9,GAD1,GABAergin neuronal maker,1,1,1,GAD1 +10,GAD2,GABAergin neuronal maker,1,1,1,GAD2 +11,ADARB2,GABAergic neurons marker,1,1,1,ADARB2 +12,SOX6,"DE in reference dataset: sstGABAnergic, pvalb GABAnergic neurons, OPC, astrocyte DE",1,1,1,SOX6 +13,SATB2,glutaminergic neuronal marker + DE in dataset,1,1,1,SATB2 +14,HS3ST4,glutaminergic neuronal marker + DE in dataset,1,1,1,HS3ST4 +15,TSHZ2,cortical neuron marker,1,1,1,TSHZ2 +16,RTN1,broad neuronal marker,1,1,1,RTN1 +17,NFIB,marker of several GABAergic neurons,1,1,1,NFIB +18,MAP2,mature neuronal marker,1,1,1,MAP2 +19,LHFPL3,DE in OPC in reference dataset,1,1,1,LHFPL3 +20,DSCAM,DE in OPC and vip-GAB in referece,1,1,1,DSCAM +21,CTNNA3,DE in oligo in reference dataset,1,1,1,CTNNA3 +22,EGFR,"GABAergic cortinal interneuron, literature + DE in reference dataset",1,1,1,EGFR +23,NXPH2,Marker of GABAergic + DE in GABAergic cortinal interneuron in reference,1,1,1,NXPH2 +24,CUX2,DE expressed in neurons reference dataset,1,1,1,CUX2 +25,RXFP1,glutaminergic neuronal marker + DE in dataset,1,1,1,RXFP1 +26,KCNIP4,interneuron and OPC + DE in reference dataset,1,1,1,KCNIP4 +27,MEF2C,Marker of glutamatergic neurons,1,1,1,MEF2C +28,CHL1,Marker of neuroplasticity/neurotropic,1,1,1,CHL1 +29,GRIK4,gene involved in synaptic signaling,1,1,1,GRIK4 +30,GRIN2B,gene involved in synaptic signaling,1,1,1,GRIN2B +31,PLP1,genes linked to myelination,1,1,1,PLP1 +32,SYT1,genes linked to calcium/calmodulin pathways,1,1,1,SYT1 +33,ATP2B2,gene linked to metabolic alteratsion,1,1,1,ATP2B2 +34,ATP1B1,gene linked to metabolic alteratsion,1,1,1,ATP1B1 +35,SYNDIG1,microglia marker gene in lit + DE in ref data,1,1,1,SYNDIG1 +36,HSP90AA1,microglial subtype marker,1,1,1,HSP90AA1 +37,ETV5,astrocyte suptype marker,1,1,1,ETV5 +38,STMN2,broad neuronal markers,1,1,1,STMN2 +39,KCNJ6,Dopaminergic neuron marker,1,1,1,KCNJ6 +40,UNC13C,GABAergic neuron marker,1,1,1,UNC13C +41,ITM2B,gene linked to B-amyloid aggregation,1,1,1,ITM2B +42,GRIA1,gene linked to glutamate transport,1,1,1,GRIA1 +43,GRIA2,gene linked to glutamate transport,1,1,1,GRIA2 +44,CAMK2A,gene linked to neurotransmitter pathways,1,1,1,CAMK2A +45,CALM2,genes linked to calcium/calmodulin pathways,1,1,1,CALM2 +46,CAMK4,genes linked to calcium/calmodulin pathways,1,1,1,CAMK4 +47,FYN,genes linked to inflammation/immune response,1,1,1,FYN +48,CALM1,genes linked to calcium/calmodulin pathways,1,1,1,CALM1 +49,ATP1A1,gene linked to metabolic alteratsion,1,1,1,ATP1A1 +50,P2RY12,homeostatic microglial gene,0,1,1,P2RY12 +51,P2RY12,activated microglial makers,0,1,1,P2RY12 +52,FGFR3,astrocyte marker literature,0,1,1,FGFR3 +53,PDGFRA,OPC maker + DE in reference dataset,0,1,1,PDGFRA +54,OPALIN,oligodendrocyte marker in literature + DE in reference dataset,0,1,1,OPALIN +55,MOG,mature oligodendrocyte marker,0,1,1,MOG +56,VIP,marker of vip GABAergic cortinal interneuron,0,1,1,VIP +57,PROX1,GABAergic cortinal interneuron marker + DE,0,1,1,PROX1 +58,SULF1,subtypes of glutaminergic neuronal also DE in dataset,0,1,1,SULF1 +59,GLUL,astrocyte marker literature,0,1,1,GLUL +60,MERTK,astrocyte suptype marker from lit,0,1,1,MERTK +61,SIRT2,cell cycle genes,0,1,1,SIRT2 +62,RGS5,pericyte marker,0,1,1,RGS5 +63,LHX6,"GABAergic cortinal interneuron, lit, DE",0,1,1,LHX6 +64,SLC17A7,glutamatergic neuron,0,1,1,SLC17A7 +65,ATP1A2,gene linked to metabolic alteratsion,0,1,1,ATP1A2 +66,BIN1,microglia marker,0,1,1,BIN1 +67,NFKB1,inflammatory microglial marker gene,0,1,1,NFKB1 +68,HIF1A,microglial subtype marker,0,1,1,HIF1A +69,LAMP1,expressed in some microglia,0,1,1,LAMP1 +70,ATP1B2,astrocyte marker literature,0,1,1,ATP1B2 +71,HOPX,oligodendrocyte subtype marker in literature,0,1,1,HOPX +72,NEFL,neuronal marker,0,1,1,NEFL +73,APOE,linked to B-amyloid aggregation,0,1,1,APOE +74,CST3,linked to B-amyloid aggregation,0,1,1,CST3 +75,SET,gene associated with neuroplasticity/neurotropic,0,1,1,SET +76,PCP4,gene associated with neuroplasticity/neurotropic,0,1,1,PCP4 +77,PTPRN,gene associated with cell-cell signaling,0,1,1,PTPRN +78,PIK3CA,gene associated with cell migration,0,1,1,PIK3CA +79,CPLX2,gene associated with synaptic signaling,0,1,1,CPLX2 +80,NDUFA4,gene linked to metabolic alteratsion,0,1,1,NDUFA4 +81,ATP5F1D,gene linked to metabolic alteratsion,0,1,1,ATP5F1D +82,MDH1,gene linked to metabolic alteratsion,0,1,1,MDH1 +83,COX4I1,gene linked to metabolic alteratsion,0,1,1,COX4I1 +84,NCAN,gene associated with biosynthesis,0,1,1,NCAN +85,RPL15,gene associated with biosynthesis,0,1,1,RPL15 +86,PSMC6,gene associated with proteosome,0,1,1,PSMC6 +87,PSMA1,gene associated with proteosome,0,1,1,PSMA1 +88,MAPT,mature neuronal marker,0,1,1,MAPT +89,ITM2C,linked to B-amyloid aggregation,0,1,1,ITM2C +90,APBB1,linked to B-amyloid aggregation,0,1,1,APBB1 +91,WASL,gene associated with cell migration,0,1,1,WASL +92,ARPC3,gene associated with cell migration,0,1,1,ARPC3 +93,SCN1B,gene associated with synaptic signaling,0,1,1,SCN1B +94,PRKCG,gene associated with neurotransmitter pathways,0,1,1,PRKCG +95,NDUFV3,gene linked to metabolic alteratsion,0,1,1,NDUFV3 +96,ATP5F1B,gene linked to metabolic alteratsion,0,1,1,ATP5F1B +97,ATP5F1A,gene linked to metabolic alteratsion,0,1,1,ATP5F1A +98,MRPL57,gene associated with biosynthesis,0,1,1,MRPL57 +99,EEF1A2,gene associated with biosynthesis,0,1,1,EEF1A2 +100,FARSB,gene associated with biosynthesis,0,1,1,FARSB diff --git a/panel_design/split/7_top150.csv b/panel_design/split/7_top150.csv new file mode 100644 index 0000000000000000000000000000000000000000..a1c2e0ac7fa50501c9d7c0506f94c4545a293cc4 --- /dev/null +++ b/panel_design/split/7_top150.csv @@ -0,0 +1,152 @@ +Unnamed: 0,gene_name,annotation,top_50,top_100,top_150,Gene Symbol +0,ADGRV1,DE in astrocyte in reference dataset,1,1,1,ADGRV1 +1,SLC1A3,astrocyte marker literature,1,1,1,SLC1A3 +2,SLC1A2,astrocyte marker literature,1,1,1,SLC1A2 +3,CDH20,"DE in Oligo, OPC, astrocyte in reference dataset",1,1,1,CDH20 +4,PTPRZ1,DE in OPC and astrocytesin reference dataset,1,1,1,PTPRZ1 +5,ST18,DE in Oligodendrocyte in reference dataset,1,1,1,ST18 +6,MBP,Oligodendrocyte marker / gene linked with methylation,1,1,1,MBP +7,PTGDS,oligodendrocyte subtype marker in literature,1,1,1,PTGDS +8,SST,marker of sstGABAergic cortinal interneuron,1,1,1,SST +9,GAD1,GABAergin neuronal maker,1,1,1,GAD1 +10,GAD2,GABAergin neuronal maker,1,1,1,GAD2 +11,ADARB2,GABAergic neurons marker,1,1,1,ADARB2 +12,SOX6,"DE in reference dataset: sstGABAnergic, pvalb GABAnergic neurons, OPC, astrocyte DE",1,1,1,SOX6 +13,SATB2,glutaminergic neuronal marker + DE in dataset,1,1,1,SATB2 +14,HS3ST4,glutaminergic neuronal marker + DE in dataset,1,1,1,HS3ST4 +15,TSHZ2,cortical neuron marker,1,1,1,TSHZ2 +16,RTN1,broad neuronal marker,1,1,1,RTN1 +17,NFIB,marker of several GABAergic neurons,1,1,1,NFIB +18,MAP2,mature neuronal marker,1,1,1,MAP2 +19,LHFPL3,DE in OPC in reference dataset,1,1,1,LHFPL3 +20,DSCAM,DE in OPC and vip-GAB in referece,1,1,1,DSCAM +21,CTNNA3,DE in oligo in reference dataset,1,1,1,CTNNA3 +22,EGFR,"GABAergic cortinal interneuron, literature + DE in reference dataset",1,1,1,EGFR +23,NXPH2,Marker of GABAergic + DE in GABAergic cortinal interneuron in reference,1,1,1,NXPH2 +24,CUX2,DE expressed in neurons reference dataset,1,1,1,CUX2 +25,RXFP1,glutaminergic neuronal marker + DE in dataset,1,1,1,RXFP1 +26,KCNIP4,interneuron and OPC + DE in reference dataset,1,1,1,KCNIP4 +27,MEF2C,Marker of glutamatergic neurons,1,1,1,MEF2C +28,CHL1,Marker of neuroplasticity/neurotropic,1,1,1,CHL1 +29,GRIK4,gene involved in synaptic signaling,1,1,1,GRIK4 +30,GRIN2B,gene involved in synaptic signaling,1,1,1,GRIN2B +31,PLP1,genes linked to myelination,1,1,1,PLP1 +32,SYT1,genes linked to calcium/calmodulin pathways,1,1,1,SYT1 +33,ATP2B2,gene linked to metabolic alteratsion,1,1,1,ATP2B2 +34,ATP1B1,gene linked to metabolic alteratsion,1,1,1,ATP1B1 +35,SYNDIG1,microglia marker gene in lit + DE in ref data,1,1,1,SYNDIG1 +36,HSP90AA1,microglial subtype marker,1,1,1,HSP90AA1 +37,ETV5,astrocyte suptype marker,1,1,1,ETV5 +38,STMN2,broad neuronal markers,1,1,1,STMN2 +39,KCNJ6,Dopaminergic neuron marker,1,1,1,KCNJ6 +40,UNC13C,GABAergic neuron marker,1,1,1,UNC13C +41,ITM2B,gene linked to B-amyloid aggregation,1,1,1,ITM2B +42,GRIA1,gene linked to glutamate transport,1,1,1,GRIA1 +43,GRIA2,gene linked to glutamate transport,1,1,1,GRIA2 +44,CAMK2A,gene linked to neurotransmitter pathways,1,1,1,CAMK2A +45,CALM2,genes linked to calcium/calmodulin pathways,1,1,1,CALM2 +46,CAMK4,genes linked to calcium/calmodulin pathways,1,1,1,CAMK4 +47,FYN,genes linked to inflammation/immune response,1,1,1,FYN +48,CALM1,genes linked to calcium/calmodulin pathways,1,1,1,CALM1 +49,ATP1A1,gene linked to metabolic alteratsion,1,1,1,ATP1A1 +50,P2RY12,homeostatic microglial gene,0,1,1,P2RY12 +51,P2RY12,activated microglial makers,0,1,1,P2RY12 +52,FGFR3,astrocyte marker literature,0,1,1,FGFR3 +53,PDGFRA,OPC maker + DE in reference dataset,0,1,1,PDGFRA +54,OPALIN,oligodendrocyte marker in literature + DE in reference dataset,0,1,1,OPALIN +55,MOG,mature oligodendrocyte marker,0,1,1,MOG +56,VIP,marker of vip GABAergic cortinal interneuron,0,1,1,VIP +57,PROX1,GABAergic cortinal interneuron marker + DE,0,1,1,PROX1 +58,SULF1,subtypes of glutaminergic neuronal also DE in dataset,0,1,1,SULF1 +59,GLUL,astrocyte marker literature,0,1,1,GLUL +60,MERTK,astrocyte suptype marker from lit,0,1,1,MERTK +61,SIRT2,cell cycle genes,0,1,1,SIRT2 +62,RGS5,pericyte marker,0,1,1,RGS5 +63,LHX6,"GABAergic cortinal interneuron, lit, DE",0,1,1,LHX6 +64,SLC17A7,glutamatergic neuron,0,1,1,SLC17A7 +65,ATP1A2,gene linked to metabolic alteratsion,0,1,1,ATP1A2 +66,BIN1,microglia marker,0,1,1,BIN1 +67,NFKB1,inflammatory microglial marker gene,0,1,1,NFKB1 +68,HIF1A,microglial subtype marker,0,1,1,HIF1A +69,LAMP1,expressed in some microglia,0,1,1,LAMP1 +70,ATP1B2,astrocyte marker literature,0,1,1,ATP1B2 +71,HOPX,oligodendrocyte subtype marker in literature,0,1,1,HOPX +72,NEFL,neuronal marker,0,1,1,NEFL +73,APOE,linked to B-amyloid aggregation,0,1,1,APOE +74,CST3,linked to B-amyloid aggregation,0,1,1,CST3 +75,SET,gene associated with neuroplasticity/neurotropic,0,1,1,SET +76,PCP4,gene associated with neuroplasticity/neurotropic,0,1,1,PCP4 +77,PTPRN,gene associated with cell-cell signaling,0,1,1,PTPRN +78,PIK3CA,gene associated with cell migration,0,1,1,PIK3CA +79,CPLX2,gene associated with synaptic signaling,0,1,1,CPLX2 +80,NDUFA4,gene linked to metabolic alteratsion,0,1,1,NDUFA4 +81,ATP5F1D,gene linked to metabolic alteratsion,0,1,1,ATP5F1D +82,MDH1,gene linked to metabolic alteratsion,0,1,1,MDH1 +83,COX4I1,gene linked to metabolic alteratsion,0,1,1,COX4I1 +84,NCAN,gene associated with biosynthesis,0,1,1,NCAN +85,RPL15,gene associated with biosynthesis,0,1,1,RPL15 +86,PSMC6,gene associated with proteosome,0,1,1,PSMC6 +87,PSMA1,gene associated with proteosome,0,1,1,PSMA1 +88,MAPT,mature neuronal marker,0,1,1,MAPT +89,ITM2C,linked to B-amyloid aggregation,0,1,1,ITM2C +90,APBB1,linked to B-amyloid aggregation,0,1,1,APBB1 +91,WASL,gene associated with cell migration,0,1,1,WASL +92,ARPC3,gene associated with cell migration,0,1,1,ARPC3 +93,SCN1B,gene associated with synaptic signaling,0,1,1,SCN1B +94,PRKCG,gene associated with neurotransmitter pathways,0,1,1,PRKCG +95,NDUFV3,gene linked to metabolic alteratsion,0,1,1,NDUFV3 +96,ATP5F1B,gene linked to metabolic alteratsion,0,1,1,ATP5F1B +97,ATP5F1A,gene linked to metabolic alteratsion,0,1,1,ATP5F1A +98,MRPL57,gene associated with biosynthesis,0,1,1,MRPL57 +99,EEF1A2,gene associated with biosynthesis,0,1,1,EEF1A2 +100,FARSB,gene associated with biosynthesis,0,1,1,FARSB +101,BLNK,microglia DE preivous paper + DE in ref data,0,0,1,BLNK +102,MRC1,"activated microglial makers in literature, DE in reference dataset",0,0,1,MRC1 +103,CD14,"microglia marked in literature, DE in reference dataset",0,0,1,CD14 +104,CX3CR1,homeostatic microglial gene,0,0,1,CX3CR1 +105,CD74,microglia marker,0,0,1,CD74 +106,SPI1,microglia marker,0,0,1,SPI1 +107,C1QB,microglia marker,0,0,1,C1QB +108,GFAP,"astrocyte marker in literature, DE in reference dataset",0,0,1,GFAP +109,AQP4,"astrocyte marker in literature, DE in reference dataset",0,0,1,AQP4 +110,AGT,astrocyte marker literature,0,0,1,AGT +111,GJB6,astrocyte marker literature,0,0,1,GJB6 +112,SOX10,oligodendrocyte marker in literature,0,0,1,SOX10 +113,OLIG1,oligodendrocyte marker in literature,0,0,1,OLIG1 +114,OLIG2,oligodendrocyte marker in literature,0,0,1,OLIG2 +115,MAG,Myelinating Oligodendrocyte Markers,0,0,1,MAG +116,KLK6,oligodendrocyte subtype marker in literature,0,0,1,KLK6 +117,ASPA,mature oligodendrocyte marker,0,0,1,ASPA +118,ITM2A,endothelial marker lit,0,0,1,ITM2A +119,PCNA,cell cycle genes,0,0,1,PCNA +120,MCM6,cell cycle genes,0,0,1,MCM6 +121,ACTA2,pericyte marker,0,0,1,ACTA2 +122,PVALB,marker of pvalb GABAergic cortinal interneuron,0,0,1,PVALB +123,LAMP5,marker of lamp5 GABAergic cortical interneuron,0,0,1,LAMP5 +124,CALB2,"vip GABAergic cortinal interneuron, literature + DE in reference dataset",0,0,1,CALB2 +125,SNCG,projecting glutaminergic cortical,0,0,1,SNCG +126,SYT6,DE in microglia in reference dataset,0,0,1,SYT6 +127,SOX9,astrocyte marker literature,0,0,1,SOX9 +128,SLC7A10,neural stem cells marker /astrocyte suptype marker from lit,0,0,1,SLC7A10 +129,ID3,astrocyte suptype marker from lit,0,0,1,ID3 +130,WFS1,astrocyte suptype marker from lit,0,0,1,WFS1 +131,FAM107A,astrocyte suptype marker from lit,0,0,1,FAM107A +132,ZNF488,mature oligodendrocyte marker,0,0,1,ZNF488 +133,CHRNA2,"vip GABAergic cortinal interneuron, literature + DE in reference dataset",0,0,1,CHRNA2 +134,PTPRC,immune marker,0,0,1,PTPRC +135,CEBPB,senescent microglia marker,0,0,1,CEBPB +136,NLRP3,,0,0,1,NLRP3 +137,CHODL,"oligodendrocyte marker in literature, DE in reference dataset",0,0,1,CHODL +138,ANXA5,oligodendrocyte subtype marker in literature,0,0,1,ANXA5 +139,OTOF,"sstGABAergic cortinal interneuron, lit, DE",0,0,1,OTOF +140,MAL,genes linked to myelination,0,0,1,MAL +141,PRKX,genes linked to inflammation/immune response,0,0,1,PRKX +142,FRZB,astrocyte suptype marker from lit,0,0,1,FRZB +143,S100B,astrocyte marker literature,0,0,1,S100B +144,NPY,Cell-cell signaling,0,0,1,NPY +145,PCDH8,Cell-cell signaling,0,0,1,PCDH8 +146,TSPAN2,genes linked to myelination,0,0,1,TSPAN2 +147,COX8A,gene linked to metabolic alteratsion,0,0,1,COX8A +148,RPN1,Proteosome,0,0,1,RPN1 +149,RELB,inflammatory microglial marker gene,0,0,1,RELB +150,NDUFS7,gene linked to metabolic alteratsion,0,0,1,NDUFS7 diff --git a/panel_design/split/7_top50.csv b/panel_design/split/7_top50.csv new file mode 100644 index 0000000000000000000000000000000000000000..bb879dbe1a3086c86bb158cfce47348e3be6a2e6 --- /dev/null +++ b/panel_design/split/7_top50.csv @@ -0,0 +1,51 @@ +Unnamed: 0,gene_name,annotation,top_50,top_100,top_150,Gene Symbol +0,ADGRV1,DE in astrocyte in reference dataset,1,1,1,ADGRV1 +1,SLC1A3,astrocyte marker literature,1,1,1,SLC1A3 +2,SLC1A2,astrocyte marker literature,1,1,1,SLC1A2 +3,CDH20,"DE in Oligo, OPC, astrocyte in reference dataset",1,1,1,CDH20 +4,PTPRZ1,DE in OPC and astrocytesin reference dataset,1,1,1,PTPRZ1 +5,ST18,DE in Oligodendrocyte in reference dataset,1,1,1,ST18 +6,MBP,Oligodendrocyte marker / gene linked with methylation,1,1,1,MBP +7,PTGDS,oligodendrocyte subtype marker in literature,1,1,1,PTGDS +8,SST,marker of sstGABAergic cortinal interneuron,1,1,1,SST +9,GAD1,GABAergin neuronal maker,1,1,1,GAD1 +10,GAD2,GABAergin neuronal maker,1,1,1,GAD2 +11,ADARB2,GABAergic neurons marker,1,1,1,ADARB2 +12,SOX6,"DE in reference dataset: sstGABAnergic, pvalb GABAnergic neurons, OPC, astrocyte DE",1,1,1,SOX6 +13,SATB2,glutaminergic neuronal marker + DE in dataset,1,1,1,SATB2 +14,HS3ST4,glutaminergic neuronal marker + DE in dataset,1,1,1,HS3ST4 +15,TSHZ2,cortical neuron marker,1,1,1,TSHZ2 +16,RTN1,broad neuronal marker,1,1,1,RTN1 +17,NFIB,marker of several GABAergic neurons,1,1,1,NFIB +18,MAP2,mature neuronal marker,1,1,1,MAP2 +19,LHFPL3,DE in OPC in reference dataset,1,1,1,LHFPL3 +20,DSCAM,DE in OPC and vip-GAB in referece,1,1,1,DSCAM +21,CTNNA3,DE in oligo in reference dataset,1,1,1,CTNNA3 +22,EGFR,"GABAergic cortinal interneuron, literature + DE in reference dataset",1,1,1,EGFR +23,NXPH2,Marker of GABAergic + DE in GABAergic cortinal interneuron in reference,1,1,1,NXPH2 +24,CUX2,DE expressed in neurons reference dataset,1,1,1,CUX2 +25,RXFP1,glutaminergic neuronal marker + DE in dataset,1,1,1,RXFP1 +26,KCNIP4,interneuron and OPC + DE in reference dataset,1,1,1,KCNIP4 +27,MEF2C,Marker of glutamatergic neurons,1,1,1,MEF2C +28,CHL1,Marker of neuroplasticity/neurotropic,1,1,1,CHL1 +29,GRIK4,gene involved in synaptic signaling,1,1,1,GRIK4 +30,GRIN2B,gene involved in synaptic signaling,1,1,1,GRIN2B +31,PLP1,genes linked to myelination,1,1,1,PLP1 +32,SYT1,genes linked to calcium/calmodulin pathways,1,1,1,SYT1 +33,ATP2B2,gene linked to metabolic alteratsion,1,1,1,ATP2B2 +34,ATP1B1,gene linked to metabolic alteratsion,1,1,1,ATP1B1 +35,SYNDIG1,microglia marker gene in lit + DE in ref data,1,1,1,SYNDIG1 +36,HSP90AA1,microglial subtype marker,1,1,1,HSP90AA1 +37,ETV5,astrocyte suptype marker,1,1,1,ETV5 +38,STMN2,broad neuronal markers,1,1,1,STMN2 +39,KCNJ6,Dopaminergic neuron marker,1,1,1,KCNJ6 +40,UNC13C,GABAergic neuron marker,1,1,1,UNC13C +41,ITM2B,gene linked to B-amyloid aggregation,1,1,1,ITM2B +42,GRIA1,gene linked to glutamate transport,1,1,1,GRIA1 +43,GRIA2,gene linked to glutamate transport,1,1,1,GRIA2 +44,CAMK2A,gene linked to neurotransmitter pathways,1,1,1,CAMK2A +45,CALM2,genes linked to calcium/calmodulin pathways,1,1,1,CALM2 +46,CAMK4,genes linked to calcium/calmodulin pathways,1,1,1,CAMK4 +47,FYN,genes linked to inflammation/immune response,1,1,1,FYN +48,CALM1,genes linked to calcium/calmodulin pathways,1,1,1,CALM1 +49,ATP1A1,gene linked to metabolic alteratsion,1,1,1,ATP1A1 diff --git a/panel_design/split/8_top100.csv b/panel_design/split/8_top100.csv new file mode 100644 index 0000000000000000000000000000000000000000..96fd7730a2e30fa4a74bf6d58fa7818721e9c0e2 --- /dev/null +++ b/panel_design/split/8_top100.csv @@ -0,0 +1,101 @@ +Unnamed: 0.1,Gene symbol,Unnamed: 0,soma_joinid,feature_id,feature_name,feature_length,nnz,n_measured_obs,highly_variable,means,dispersions,dispersions_norm,Annotation & Reasoning,Ranking +0,NPY,0,5241,ENSG00000122585,NPY,893,1487637,69587951,True,0.2784628391503804,5.472432619245862,8.076506,Based on the hvgs with best normalization method,top50 +1,DOCK8,1,3360,ENSG00000107099,DOCK8,20056,11548217,74513630,True,0.52630464178451,4.0060010387337766,6.769335,Based on the hvgs with best normalization method,top50 +2,APBB1IP,2,1377,ENSG00000077420,APBB1IP,3843,11013041,74541465,True,0.46146377718864,3.8968569972808864,6.524311,Based on the hvgs with best normalization method,top50 +3,OBI1-AS1,3,22073,ENSG00000234377,OBI1-AS1,10180,2521757,61741141,True,0.9734908521337996,3.958334930562165,6.3384104,Based on the hvgs with best normalization method,top50 +4,CCL3,4,32293,ENSG00000277632,CCL3,1494,3866143,61139156,True,0.0093135161911686,4.393203181327225,6.1793547,Based on the hvgs with best normalization method,top50 +5,MOBP,5,12173,ENSG00000168314,MOBP,9237,4846625,72513409,True,0.9614663545220036,3.858070407540572,6.1201987,Based on the hvgs with best normalization method,top50 +6,SPP1,6,4810,ENSG00000118785,SPP1,7250,8595089,73920484,True,0.5587869816521938,3.706557523761285,6.0970974,Based on the hvgs with best normalization method,top50 +7,PTGDS,7,3383,ENSG00000107317,PTGDS,2712,10225627,74204733,True,1.2863731638684015,3.751850425826654,5.836781,Based on the hvgs with best normalization method,top50 +8,HPSE2,8,13218,ENSG00000172987,HPSE2,4421,3904787,73047912,True,1.2129782158058935,3.7012206035381774,5.724114,Based on the hvgs with best normalization method,top50 +9,ATP1A2,9,381,ENSG00000018625,ATP1A2,6298,4848403,73460061,True,0.7347201184171539,3.663378427183657,5.696479,Based on the hvgs with best normalization method,top50 +10,VCAN,10,548,ENSG00000038427,VCAN,14678,12407214,74552031,True,0.7631149795408386,3.600036576582366,5.5586243,Based on the hvgs with best normalization method,top50 +11,HSPA1A,11,17512,ENSG00000204389,HSPA1A,2404,20330619,64819739,True,0.3483590391423253,3.464540264521311,5.553779,Based on the hvgs with best normalization method,top50 +12,HTR2C,12,8855,ENSG00000147246,HTR2C,4842,3271887,58827748,True,0.7858794209017294,3.5926348724054007,5.5425153,Based on the hvgs with best normalization method,top50 +13,LINC00499,13,25247,ENSG00000251372,LINC00499,20131,1680795,57253942,True,0.5150609102577905,3.4490050961280407,5.5189033,Based on the hvgs with best normalization method,top50 +14,CERCAM,14,11877,ENSG00000167123,CERCAM,6058,6253645,74320849,True,0.7828653201335859,3.565729125607589,5.4839587,Based on the hvgs with best normalization method,top50 +15,P2RY12,15,12400,ENSG00000169313,P2RY12,2337,2640423,71547277,True,0.4689977371721238,3.4120002061266947,5.435829,Based on the hvgs with best normalization method,top50 +16,GFAP,16,6331,ENSG00000131095,GFAP,11229,2603965,73350833,True,0.2876280341852898,3.936925670728887,5.377276,Based on the hvgs with best normalization method,top50 +17,SLC5A11,17,10161,ENSG00000158865,SLC5A11,3415,3364055,69923585,True,0.574600980890718,3.376484619946382,5.356098,Based on the hvgs with best normalization method,top50 +18,CNDP1,18,9219,ENSG00000150656,CNDP1,7117,3107345,74297237,True,0.5260503757110235,3.324706229330675,5.239858,Based on the hvgs with best normalization method,top50 +19,XIST,19,20711,ENSG00000229807,XIST,25266,21056286,74315539,True,1.1813301289979623,3.466603367504034,5.2020183,Based on the hvgs with best normalization method,top50 +20,TMEM144,20,11123,ENSG00000164124,TMEM144,9248,8045941,74437632,True,0.9652175529364386,3.4310867041448723,5.1909285,Based on the hvgs with best normalization method,top50 +21,CCL4,21,31901,ENSG00000275302,CCL4,1795,6143173,62760344,True,0.0025910273861521,3.8228119051090586,5.176678,Based on the hvgs with best normalization method,top50 +22,CLDN5,22,15192,ENSG00000184113,CLDN5,3429,2524056,74446360,True,0.0437600914665603,3.819325395509967,5.1705494,Based on the hvgs with best normalization method,top50 +23,LINC01170,23,25766,ENSG00000253807,LINC01170,3850,3703423,68171161,True,0.5707032423248326,3.285253035209508,5.151287,Based on the hvgs with best normalization method,top50 +24,HERC2P3_ENSG00000180229,24,33730,ENSG00000180229,HERC2P3_ENSG00000180229,4088,1827785,27752448,True,0.2803260141440332,3.767946887391629,5.080232,Based on the hvgs with best normalization method,top50 +25,COL5A3,25,1491,ENSG00000080573,COL5A3,6783,5447597,74449798,True,0.7555858023716054,3.378410803350078,5.076287,Based on the hvgs with best normalization method,top50 +26,C10orf90,26,9666,ENSG00000154493,C10orf90,5659,4440265,67152709,True,0.875581664155515,3.374201105219235,5.0671253,Based on the hvgs with best normalization method,top50 +27,VIP,27,8761,ENSG00000146469,VIP,1585,1138785,68691606,True,0.7213746131758096,3.3694517010030616,5.056789,Based on the hvgs with best normalization method,top50 +28,DOCK5,28,8882,ENSG00000147459,DOCK5,15989,10823963,74290291,True,0.9082107891458808,3.368977884127573,5.0557575,Based on the hvgs with best normalization method,top50 +29,LINC00609,29,27091,ENSG00000257585,LINC00609,583,4992807,50838830,True,0.7328977593171419,3.348563758499239,5.011329,Based on the hvgs with best normalization method,top50 +30,ENPP2,30,7309,ENSG00000136960,ENPP2,6729,6966932,74560519,True,0.7944294216609206,3.333032659221951,4.9775276,Based on the hvgs with best normalization method,top50 +31,THSD7B,31,8449,ENSG00000144229,THSD7B,6708,6341082,70419221,True,0.9194696182982336,3.310702524025904,4.9289293,Based on the hvgs with best normalization method,top50 +32,KCNH8,32,15168,ENSG00000183960,KCNH8,6088,6157298,74266159,True,0.883848466775706,3.3051588757014865,4.9168644,Based on the hvgs with best normalization method,top50 +33,CST3,33,2510,ENSG00000101439,CST3,3615,27512197,74668992,True,0.6748217603618987,3.1795989384367074,4.9140983,Based on the hvgs with best normalization method,top50 +34,EBF1,34,11191,ENSG00000164330,EBF1,6316,6862033,74452348,True,0.0808396016860488,3.669852728684886,4.907795,Based on the hvgs with best normalization method,top50 +35,ZFP36L1,35,15531,ENSG00000185650,ZFP36L1,6466,26391142,74394567,True,0.3487557774474227,3.173621365905224,4.900679,Based on the hvgs with best normalization method,top50 +36,FAM177B,36,16783,ENSG00000197520,FAM177B,2805,1011468,74266705,True,0.1702217044119632,3.648502115335857,4.870263,Based on the hvgs with best normalization method,top50 +37,SST,37,9936,ENSG00000157005,SST,607,2166462,63111103,True,0.794279151054743,3.2809449870199447,4.8641663,Based on the hvgs with best normalization method,top50 +38,MOG,38,17608,ENSG00000204655,MOG,3175,2662310,63168628,True,0.5889720942517631,3.155045946295618,4.8589783,Based on the hvgs with best normalization method,top50 +39,ID3,39,4659,ENSG00000117318,ID3,1496,10773972,72735199,True,0.11467277785575,3.636231982980021,4.848694,Based on the hvgs with best normalization method,top50 +40,CARNS1,40,13126,ENSG00000172508,CARNS1,5670,2858746,74329638,True,0.4607257690794789,3.13743267495587,4.819437,Based on the hvgs with best normalization method,top50 +41,NHSL1,41,7026,ENSG00000135540,NHSL1,8741,8516367,74564094,True,0.6323054715980764,3.1336152777375865,4.8108673,Based on the hvgs with best normalization method,top50 +42,SLC1A2,42,3748,ENSG00000110436,SLC1A2,22800,14723654,74245583,True,2.2372810686961864,5.118459519668878,4.8069806,Based on the hvgs with best normalization method,top50 +43,SCGB1B2P,43,30347,ENSG00000268751,SCGB1B2P,754,513073,71291662,True,0.0023219452044399,3.596334101249868,4.7785583,Based on the hvgs with best normalization method,top50 +44,OLIG1,44,15219,ENSG00000184221,OLIG1,2273,2770870,73073562,True,0.3798056667882042,3.103276777107508,4.7427588,Based on the hvgs with best normalization method,top50 +45,UGT8,45,13504,ENSG00000174607,UGT8,4385,4502125,74250259,True,0.5682002703328997,3.0803347057375188,4.6912546,Based on the hvgs with best normalization method,top50 +46,OPALIN,46,16760,ENSG00000197430,OPALIN,3874,1835848,56197923,True,0.4221487818214699,3.0707599776788013,4.6697598,Based on the hvgs with best normalization method,top50 +47,FRMD4B,47,4266,ENSG00000114541,FRMD4B,11201,16560570,74505631,True,1.222521920193563,3.2186813547572046,4.6503153,Based on the hvgs with best normalization method,top50 +48,CLDN11,48,327,ENSG00000013297,CLDN11,4321,3987554,72517586,True,0.5142272418796419,3.052022016031001,4.6276937,Based on the hvgs with best normalization method,top50 +49,MAG,49,3147,ENSG00000105695,MAG,2960,2791162,73743438,True,0.4186199035688159,3.0412205225704634,4.603445,Based on the hvgs with best normalization method,top50 +50,PLP1,50,5351,ENSG00000123560,PLP1,6088,6982636,70276834,True,1.704900313728858,4.6528393089055085,4.5909967,Based on the hvgs with best normalization method,top100 +51,SLCO1A2,51,1618,ENSG00000084453,SLCO1A2,11524,2884484,71671661,True,0.4585991876673033,3.028782719125683,4.5755224,Based on the hvgs with best normalization method,top100 +52,APOE,52,6161,ENSG00000130203,APOE,2154,12430586,74637406,True,0.4222819610948677,3.0165719918308875,4.54811,Based on the hvgs with best normalization method,top100 +53,CNR1,53,4773,ENSG00000118432,CNR1,6345,8991633,74294563,True,1.265042676927463,3.169683594582363,4.5412803,Based on the hvgs with best normalization method,top100 +54,BCAS1,54,895,ENSG00000064787,BCAS1,10533,4663794,74474849,True,0.7391818799326076,3.119855545502296,4.513578,Based on the hvgs with best normalization method,top100 +55,ABCA8,55,8003,ENSG00000141338,ABCA8,11246,5532225,72981313,True,0.4489079817334538,2.998058009662332,4.506547,Based on the hvgs with best normalization method,top100 +56,LPAR1,56,16969,ENSG00000198121,LPAR1,4137,6467341,74560584,True,0.7618253824496256,3.1147901955503943,4.502554,Based on the hvgs with best normalization method,top100 +57,CX3CR1,57,12174,ENSG00000168329,CX3CR1,3656,2537803,74287878,True,0.204655545268738,3.438573298048609,4.5012345,Based on the hvgs with best normalization method,top100 +58,ST6GAL1,58,1237,ENSG00000073849,ST6GAL1,11142,15534400,74572847,True,1.1982050631253789,3.135386706349258,4.464959,Based on the hvgs with best normalization method,top100 +59,ST18,59,8888,ENSG00000147488,ST18,14438,6471842,73827740,True,1.577247400151292,4.546193079758141,4.440893,Based on the hvgs with best normalization method,top100 +60,TF,60,1919,ENSG00000091513,TF,26038,9640434,74021614,True,1.3027290464455048,3.118480425787,4.427337,Based on the hvgs with best normalization method,top100 +61,COLEC12,61,10082,ENSG00000158270,COLEC12,7343,5421572,74526534,True,0.1283533079912794,3.3910721924048253,4.4177337,Based on the hvgs with best normalization method,top100 +62,RELN,62,16276,ENSG00000189056,RELN,35421,5989024,72845284,True,1.482391183112545,4.527350413214273,4.4143724,Based on the hvgs with best normalization method,top100 +63,MIR7706,63,57505,ENSG00000284160,MIR7706,67,320,4558058,True,0.000254312790952,3.3613201936542554,4.365433,Based on the hvgs with best normalization method,top100 +64,PDGFRA,64,6893,ENSG00000134853,PDGFRA,9547,4133848,74205232,True,0.4095718553912153,2.9347512477217723,4.364426,Based on the hvgs with best normalization method,top100 +65,SHROOM4,65,10090,ENSG00000158352,SHROOM4,15184,4356923,68572200,True,0.5218087821649494,2.934423219412552,4.36369,Based on the hvgs with best normalization method,top100 +66,FAM107B,66,942,ENSG00000065809,FAM107B,7019,17633084,74572847,True,0.5555093594169203,2.9341386699319374,4.363051,Based on the hvgs with best normalization method,top100 +67,MT2A,67,5560,ENSG00000125148,MT2A,914,24572055,74524461,True,0.2522153061080633,3.3475597892810303,4.341244,Based on the hvgs with best normalization method,top100 +68,FOLH1,68,1679,ENSG00000086205,FOLH1,5335,2928011,71970665,True,0.3628899572923751,2.9220987639981115,4.336022,Based on the hvgs with best normalization method,top100 +69,RGS1,69,1855,ENSG00000090104,RGS1,4074,6643913,74375774,True,0.0293400730240709,3.3439888948949217,4.334967,Based on the hvgs with best normalization method,top100 +70,SLCO2B1,70,7396,ENSG00000137491,SLCO2B1,10277,4449905,74236127,True,0.2450073189899416,3.340218262666361,4.3283386,Based on the hvgs with best normalization method,top100 +71,FGFR3,71,1025,ENSG00000068078,FGFR3,4848,2468727,73293979,True,0.3871606803865989,2.917813419730002,4.326401,Based on the hvgs with best normalization method,top100 +72,PLA2G2D,72,4646,ENSG00000117215,PLA2G2D,2681,80697,73080509,True,0.0005875360712289,3.335563492475841,4.320156,Based on the hvgs with best normalization method,top100 +73,VRK2,73,464,ENSG00000028116,VRK2,3531,9129288,74502763,True,0.4571411935658678,2.911487996657224,4.312201,Based on the hvgs with best normalization method,top100 +74,ZFP36L2,74,9446,ENSG00000152518,ZFP36L2,3693,27332772,74216795,True,0.31513478008652,3.32672126012961,4.3046126,Based on the hvgs with best normalization method,top100 +75,COL4A5,75,16071,ENSG00000188153,COL4A5,11871,6328106,73114575,True,0.5128218347917031,2.902175786769859,4.2912955,Based on the hvgs with best normalization method,top100 +76,SELENOP,76,25021,ENSG00000250722,SELENOP,5502,12343990,66178131,True,0.3831500387134665,2.899454711442124,4.285187,Based on the hvgs with best normalization method,top100 +77,LINC00639,77,27678,ENSG00000259070,LINC00639,9453,2985688,69780519,True,0.4345296954736012,2.896433940788761,4.2784057,Based on the hvgs with best normalization method,top100 +78,GLUL,78,7072,ENSG00000135821,GLUL,12638,24312926,74400727,True,0.6637534522594922,2.8934367421432725,4.271677,Based on the hvgs with best normalization method,top100 +79,AOAH,79,7168,ENSG00000136250,AOAH,3518,9127086,74627767,True,0.4765791257977578,2.890629823868756,4.2653756,Based on the hvgs with best normalization method,top100 +80,DAAM2,80,8714,ENSG00000146122,DAAM2,12955,5616378,74113794,True,0.6971061682850775,2.9942330891377824,4.240178,Based on the hvgs with best normalization method,top100 +81,C3_ENSG00000125730,81,5639,ENSG00000125730,C3_ENSG00000125730,11577,5625071,74572198,True,0.1863441181735022,3.2771579168293976,4.2174864,Based on the hvgs with best normalization method,top100 +82,SAMSN1,82,9748,ENSG00000155307,SAMSN1,5185,9277875,74484680,True,0.1796204277436855,3.27512934432833,4.2139206,Based on the hvgs with best normalization method,top100 +83,FA2H,83,2741,ENSG00000103089,FA2H,3279,3529192,74201872,True,0.4488143386102362,2.855949955123439,4.1875205,Based on the hvgs with best normalization method,top100 +84,CNP,84,13370,ENSG00000173786,CNP,7413,10634612,68068310,True,0.5775932053867758,2.850697087469193,4.1757283,Based on the hvgs with best normalization method,top100 +85,A2M,85,13735,ENSG00000175899,A2M,6318,9578251,74374953,True,0.3597823122995064,2.8493397666909885,4.1726813,Based on the hvgs with best normalization method,top100 +86,EYA4,86,4017,ENSG00000112319,EYA4,14674,4511586,72476380,True,0.6717625269274267,2.847782217464863,4.1691847,Based on the hvgs with best normalization method,top100 +87,SLC1A3,87,1444,ENSG00000079215,SLC1A3,21227,9582156,74406585,True,1.4390292471685913,4.344737590726089,4.157347,Based on the hvgs with best normalization method,top100 +88,PREX2,88,609,ENSG00000046889,PREX2,12132,8642053,74004383,True,1.0206908773132053,2.9471314964557203,4.137668,Based on the hvgs with best normalization method,top100 +89,CSF1R,89,14858,ENSG00000182578,CSF1R,5151,4291984,74457424,True,0.2114800007063883,3.229929765088273,4.134465,Based on the hvgs with best normalization method,top100 +90,LINC00299,90,22824,ENSG00000236790,LINC00299,23624,6051694,71833857,True,1.207794284898008,2.98642125462354,4.133465,Based on the hvgs with best normalization method,top100 +91,NDRG2,91,11540,ENSG00000165795,NDRG2,7550,9251140,74333224,True,0.484050325408196,2.826831765286455,4.122152,Based on the hvgs with best normalization method,top100 +92,PAMR1,92,9070,ENSG00000149090,PAMR1,3861,4123680,68354158,True,0.6077380143589539,2.8248041935470094,4.1176,Based on the hvgs with best normalization method,top100 +93,ADGRV1,93,11151,ENSG00000164199,ADGRV1,33822,11676625,65661938,True,1.5122817302150076,4.310319621834209,4.1089044,Based on the hvgs with best normalization method,top100 +94,FLT1,94,2679,ENSG00000102755,FLT1,12575,4248956,74491361,True,0.176550708931357,3.2082475484185897,4.0963507,Based on the hvgs with best normalization method,top100 +95,INPP5D,95,12305,ENSG00000168918,INPP5D,8681,8098619,73331347,True,0.316509087020153,3.2078499774165667,4.0956516,Based on the hvgs with best normalization method,top100 +96,RANBP3L,96,11148,ENSG00000164188,RANBP3L,4884,2795980,72610757,True,0.4411228413386047,2.8144874319792192,4.094439,Based on the hvgs with best normalization method,top100 +97,CREB5,97,8772,ENSG00000146592,CREB5,11681,13818231,74523823,True,1.0205085582340396,2.922207803928156,4.083425,Based on the hvgs with best normalization method,top100 +98,SEMA5A,98,4089,ENSG00000112902,SEMA5A,12308,9671805,74530046,True,0.9880056438061656,2.920265662984906,4.0791984,Based on the hvgs with best normalization method,top100 +99,MYRF,99,5548,ENSG00000124920,MYRF,10773,3322912,74238984,True,0.3687125871338419,2.7983769104151883,4.058272,Based on the hvgs with best normalization method,top100 diff --git a/panel_design/split/8_top150.csv b/panel_design/split/8_top150.csv new file mode 100644 index 0000000000000000000000000000000000000000..b37b43a09f9eabb313270331eacedd02d924a4dc --- /dev/null +++ b/panel_design/split/8_top150.csv @@ -0,0 +1,151 @@ +Unnamed: 0.1,Gene symbol,Unnamed: 0,soma_joinid,feature_id,feature_name,feature_length,nnz,n_measured_obs,highly_variable,means,dispersions,dispersions_norm,Annotation & Reasoning,Ranking +0,NPY,0,5241,ENSG00000122585,NPY,893,1487637,69587951,True,0.2784628391503804,5.472432619245862,8.076506,Based on the hvgs with best normalization method,top50 +1,DOCK8,1,3360,ENSG00000107099,DOCK8,20056,11548217,74513630,True,0.52630464178451,4.0060010387337766,6.769335,Based on the hvgs with best normalization method,top50 +2,APBB1IP,2,1377,ENSG00000077420,APBB1IP,3843,11013041,74541465,True,0.46146377718864,3.8968569972808864,6.524311,Based on the hvgs with best normalization method,top50 +3,OBI1-AS1,3,22073,ENSG00000234377,OBI1-AS1,10180,2521757,61741141,True,0.9734908521337996,3.958334930562165,6.3384104,Based on the hvgs with best normalization method,top50 +4,CCL3,4,32293,ENSG00000277632,CCL3,1494,3866143,61139156,True,0.0093135161911686,4.393203181327225,6.1793547,Based on the hvgs with best normalization method,top50 +5,MOBP,5,12173,ENSG00000168314,MOBP,9237,4846625,72513409,True,0.9614663545220036,3.858070407540572,6.1201987,Based on the hvgs with best normalization method,top50 +6,SPP1,6,4810,ENSG00000118785,SPP1,7250,8595089,73920484,True,0.5587869816521938,3.706557523761285,6.0970974,Based on the hvgs with best normalization method,top50 +7,PTGDS,7,3383,ENSG00000107317,PTGDS,2712,10225627,74204733,True,1.2863731638684015,3.751850425826654,5.836781,Based on the hvgs with best normalization method,top50 +8,HPSE2,8,13218,ENSG00000172987,HPSE2,4421,3904787,73047912,True,1.2129782158058935,3.7012206035381774,5.724114,Based on the hvgs with best normalization method,top50 +9,ATP1A2,9,381,ENSG00000018625,ATP1A2,6298,4848403,73460061,True,0.7347201184171539,3.663378427183657,5.696479,Based on the hvgs with best normalization method,top50 +10,VCAN,10,548,ENSG00000038427,VCAN,14678,12407214,74552031,True,0.7631149795408386,3.600036576582366,5.5586243,Based on the hvgs with best normalization method,top50 +11,HSPA1A,11,17512,ENSG00000204389,HSPA1A,2404,20330619,64819739,True,0.3483590391423253,3.464540264521311,5.553779,Based on the hvgs with best normalization method,top50 +12,HTR2C,12,8855,ENSG00000147246,HTR2C,4842,3271887,58827748,True,0.7858794209017294,3.5926348724054007,5.5425153,Based on the hvgs with best normalization method,top50 +13,LINC00499,13,25247,ENSG00000251372,LINC00499,20131,1680795,57253942,True,0.5150609102577905,3.4490050961280407,5.5189033,Based on the hvgs with best normalization method,top50 +14,CERCAM,14,11877,ENSG00000167123,CERCAM,6058,6253645,74320849,True,0.7828653201335859,3.565729125607589,5.4839587,Based on the hvgs with best normalization method,top50 +15,P2RY12,15,12400,ENSG00000169313,P2RY12,2337,2640423,71547277,True,0.4689977371721238,3.4120002061266947,5.435829,Based on the hvgs with best normalization method,top50 +16,GFAP,16,6331,ENSG00000131095,GFAP,11229,2603965,73350833,True,0.2876280341852898,3.936925670728887,5.377276,Based on the hvgs with best normalization method,top50 +17,SLC5A11,17,10161,ENSG00000158865,SLC5A11,3415,3364055,69923585,True,0.574600980890718,3.376484619946382,5.356098,Based on the hvgs with best normalization method,top50 +18,CNDP1,18,9219,ENSG00000150656,CNDP1,7117,3107345,74297237,True,0.5260503757110235,3.324706229330675,5.239858,Based on the hvgs with best normalization method,top50 +19,XIST,19,20711,ENSG00000229807,XIST,25266,21056286,74315539,True,1.1813301289979623,3.466603367504034,5.2020183,Based on the hvgs with best normalization method,top50 +20,TMEM144,20,11123,ENSG00000164124,TMEM144,9248,8045941,74437632,True,0.9652175529364386,3.4310867041448723,5.1909285,Based on the hvgs with best normalization method,top50 +21,CCL4,21,31901,ENSG00000275302,CCL4,1795,6143173,62760344,True,0.0025910273861521,3.8228119051090586,5.176678,Based on the hvgs with best normalization method,top50 +22,CLDN5,22,15192,ENSG00000184113,CLDN5,3429,2524056,74446360,True,0.0437600914665603,3.819325395509967,5.1705494,Based on the hvgs with best normalization method,top50 +23,LINC01170,23,25766,ENSG00000253807,LINC01170,3850,3703423,68171161,True,0.5707032423248326,3.285253035209508,5.151287,Based on the hvgs with best normalization method,top50 +24,HERC2P3_ENSG00000180229,24,33730,ENSG00000180229,HERC2P3_ENSG00000180229,4088,1827785,27752448,True,0.2803260141440332,3.767946887391629,5.080232,Based on the hvgs with best normalization method,top50 +25,COL5A3,25,1491,ENSG00000080573,COL5A3,6783,5447597,74449798,True,0.7555858023716054,3.378410803350078,5.076287,Based on the hvgs with best normalization method,top50 +26,C10orf90,26,9666,ENSG00000154493,C10orf90,5659,4440265,67152709,True,0.875581664155515,3.374201105219235,5.0671253,Based on the hvgs with best normalization method,top50 +27,VIP,27,8761,ENSG00000146469,VIP,1585,1138785,68691606,True,0.7213746131758096,3.3694517010030616,5.056789,Based on the hvgs with best normalization method,top50 +28,DOCK5,28,8882,ENSG00000147459,DOCK5,15989,10823963,74290291,True,0.9082107891458808,3.368977884127573,5.0557575,Based on the hvgs with best normalization method,top50 +29,LINC00609,29,27091,ENSG00000257585,LINC00609,583,4992807,50838830,True,0.7328977593171419,3.348563758499239,5.011329,Based on the hvgs with best normalization method,top50 +30,ENPP2,30,7309,ENSG00000136960,ENPP2,6729,6966932,74560519,True,0.7944294216609206,3.333032659221951,4.9775276,Based on the hvgs with best normalization method,top50 +31,THSD7B,31,8449,ENSG00000144229,THSD7B,6708,6341082,70419221,True,0.9194696182982336,3.310702524025904,4.9289293,Based on the hvgs with best normalization method,top50 +32,KCNH8,32,15168,ENSG00000183960,KCNH8,6088,6157298,74266159,True,0.883848466775706,3.3051588757014865,4.9168644,Based on the hvgs with best normalization method,top50 +33,CST3,33,2510,ENSG00000101439,CST3,3615,27512197,74668992,True,0.6748217603618987,3.1795989384367074,4.9140983,Based on the hvgs with best normalization method,top50 +34,EBF1,34,11191,ENSG00000164330,EBF1,6316,6862033,74452348,True,0.0808396016860488,3.669852728684886,4.907795,Based on the hvgs with best normalization method,top50 +35,ZFP36L1,35,15531,ENSG00000185650,ZFP36L1,6466,26391142,74394567,True,0.3487557774474227,3.173621365905224,4.900679,Based on the hvgs with best normalization method,top50 +36,FAM177B,36,16783,ENSG00000197520,FAM177B,2805,1011468,74266705,True,0.1702217044119632,3.648502115335857,4.870263,Based on the hvgs with best normalization method,top50 +37,SST,37,9936,ENSG00000157005,SST,607,2166462,63111103,True,0.794279151054743,3.2809449870199447,4.8641663,Based on the hvgs with best normalization method,top50 +38,MOG,38,17608,ENSG00000204655,MOG,3175,2662310,63168628,True,0.5889720942517631,3.155045946295618,4.8589783,Based on the hvgs with best normalization method,top50 +39,ID3,39,4659,ENSG00000117318,ID3,1496,10773972,72735199,True,0.11467277785575,3.636231982980021,4.848694,Based on the hvgs with best normalization method,top50 +40,CARNS1,40,13126,ENSG00000172508,CARNS1,5670,2858746,74329638,True,0.4607257690794789,3.13743267495587,4.819437,Based on the hvgs with best normalization method,top50 +41,NHSL1,41,7026,ENSG00000135540,NHSL1,8741,8516367,74564094,True,0.6323054715980764,3.1336152777375865,4.8108673,Based on the hvgs with best normalization method,top50 +42,SLC1A2,42,3748,ENSG00000110436,SLC1A2,22800,14723654,74245583,True,2.2372810686961864,5.118459519668878,4.8069806,Based on the hvgs with best normalization method,top50 +43,SCGB1B2P,43,30347,ENSG00000268751,SCGB1B2P,754,513073,71291662,True,0.0023219452044399,3.596334101249868,4.7785583,Based on the hvgs with best normalization method,top50 +44,OLIG1,44,15219,ENSG00000184221,OLIG1,2273,2770870,73073562,True,0.3798056667882042,3.103276777107508,4.7427588,Based on the hvgs with best normalization method,top50 +45,UGT8,45,13504,ENSG00000174607,UGT8,4385,4502125,74250259,True,0.5682002703328997,3.0803347057375188,4.6912546,Based on the hvgs with best normalization method,top50 +46,OPALIN,46,16760,ENSG00000197430,OPALIN,3874,1835848,56197923,True,0.4221487818214699,3.0707599776788013,4.6697598,Based on the hvgs with best normalization method,top50 +47,FRMD4B,47,4266,ENSG00000114541,FRMD4B,11201,16560570,74505631,True,1.222521920193563,3.2186813547572046,4.6503153,Based on the hvgs with best normalization method,top50 +48,CLDN11,48,327,ENSG00000013297,CLDN11,4321,3987554,72517586,True,0.5142272418796419,3.052022016031001,4.6276937,Based on the hvgs with best normalization method,top50 +49,MAG,49,3147,ENSG00000105695,MAG,2960,2791162,73743438,True,0.4186199035688159,3.0412205225704634,4.603445,Based on the hvgs with best normalization method,top50 +50,PLP1,50,5351,ENSG00000123560,PLP1,6088,6982636,70276834,True,1.704900313728858,4.6528393089055085,4.5909967,Based on the hvgs with best normalization method,top100 +51,SLCO1A2,51,1618,ENSG00000084453,SLCO1A2,11524,2884484,71671661,True,0.4585991876673033,3.028782719125683,4.5755224,Based on the hvgs with best normalization method,top100 +52,APOE,52,6161,ENSG00000130203,APOE,2154,12430586,74637406,True,0.4222819610948677,3.0165719918308875,4.54811,Based on the hvgs with best normalization method,top100 +53,CNR1,53,4773,ENSG00000118432,CNR1,6345,8991633,74294563,True,1.265042676927463,3.169683594582363,4.5412803,Based on the hvgs with best normalization method,top100 +54,BCAS1,54,895,ENSG00000064787,BCAS1,10533,4663794,74474849,True,0.7391818799326076,3.119855545502296,4.513578,Based on the hvgs with best normalization method,top100 +55,ABCA8,55,8003,ENSG00000141338,ABCA8,11246,5532225,72981313,True,0.4489079817334538,2.998058009662332,4.506547,Based on the hvgs with best normalization method,top100 +56,LPAR1,56,16969,ENSG00000198121,LPAR1,4137,6467341,74560584,True,0.7618253824496256,3.1147901955503943,4.502554,Based on the hvgs with best normalization method,top100 +57,CX3CR1,57,12174,ENSG00000168329,CX3CR1,3656,2537803,74287878,True,0.204655545268738,3.438573298048609,4.5012345,Based on the hvgs with best normalization method,top100 +58,ST6GAL1,58,1237,ENSG00000073849,ST6GAL1,11142,15534400,74572847,True,1.1982050631253789,3.135386706349258,4.464959,Based on the hvgs with best normalization method,top100 +59,ST18,59,8888,ENSG00000147488,ST18,14438,6471842,73827740,True,1.577247400151292,4.546193079758141,4.440893,Based on the hvgs with best normalization method,top100 +60,TF,60,1919,ENSG00000091513,TF,26038,9640434,74021614,True,1.3027290464455048,3.118480425787,4.427337,Based on the hvgs with best normalization method,top100 +61,COLEC12,61,10082,ENSG00000158270,COLEC12,7343,5421572,74526534,True,0.1283533079912794,3.3910721924048253,4.4177337,Based on the hvgs with best normalization method,top100 +62,RELN,62,16276,ENSG00000189056,RELN,35421,5989024,72845284,True,1.482391183112545,4.527350413214273,4.4143724,Based on the hvgs with best normalization method,top100 +63,MIR7706,63,57505,ENSG00000284160,MIR7706,67,320,4558058,True,0.000254312790952,3.3613201936542554,4.365433,Based on the hvgs with best normalization method,top100 +64,PDGFRA,64,6893,ENSG00000134853,PDGFRA,9547,4133848,74205232,True,0.4095718553912153,2.9347512477217723,4.364426,Based on the hvgs with best normalization method,top100 +65,SHROOM4,65,10090,ENSG00000158352,SHROOM4,15184,4356923,68572200,True,0.5218087821649494,2.934423219412552,4.36369,Based on the hvgs with best normalization method,top100 +66,FAM107B,66,942,ENSG00000065809,FAM107B,7019,17633084,74572847,True,0.5555093594169203,2.9341386699319374,4.363051,Based on the hvgs with best normalization method,top100 +67,MT2A,67,5560,ENSG00000125148,MT2A,914,24572055,74524461,True,0.2522153061080633,3.3475597892810303,4.341244,Based on the hvgs with best normalization method,top100 +68,FOLH1,68,1679,ENSG00000086205,FOLH1,5335,2928011,71970665,True,0.3628899572923751,2.9220987639981115,4.336022,Based on the hvgs with best normalization method,top100 +69,RGS1,69,1855,ENSG00000090104,RGS1,4074,6643913,74375774,True,0.0293400730240709,3.3439888948949217,4.334967,Based on the hvgs with best normalization method,top100 +70,SLCO2B1,70,7396,ENSG00000137491,SLCO2B1,10277,4449905,74236127,True,0.2450073189899416,3.340218262666361,4.3283386,Based on the hvgs with best normalization method,top100 +71,FGFR3,71,1025,ENSG00000068078,FGFR3,4848,2468727,73293979,True,0.3871606803865989,2.917813419730002,4.326401,Based on the hvgs with best normalization method,top100 +72,PLA2G2D,72,4646,ENSG00000117215,PLA2G2D,2681,80697,73080509,True,0.0005875360712289,3.335563492475841,4.320156,Based on the hvgs with best normalization method,top100 +73,VRK2,73,464,ENSG00000028116,VRK2,3531,9129288,74502763,True,0.4571411935658678,2.911487996657224,4.312201,Based on the hvgs with best normalization method,top100 +74,ZFP36L2,74,9446,ENSG00000152518,ZFP36L2,3693,27332772,74216795,True,0.31513478008652,3.32672126012961,4.3046126,Based on the hvgs with best normalization method,top100 +75,COL4A5,75,16071,ENSG00000188153,COL4A5,11871,6328106,73114575,True,0.5128218347917031,2.902175786769859,4.2912955,Based on the hvgs with best normalization method,top100 +76,SELENOP,76,25021,ENSG00000250722,SELENOP,5502,12343990,66178131,True,0.3831500387134665,2.899454711442124,4.285187,Based on the hvgs with best normalization method,top100 +77,LINC00639,77,27678,ENSG00000259070,LINC00639,9453,2985688,69780519,True,0.4345296954736012,2.896433940788761,4.2784057,Based on the hvgs with best normalization method,top100 +78,GLUL,78,7072,ENSG00000135821,GLUL,12638,24312926,74400727,True,0.6637534522594922,2.8934367421432725,4.271677,Based on the hvgs with best normalization method,top100 +79,AOAH,79,7168,ENSG00000136250,AOAH,3518,9127086,74627767,True,0.4765791257977578,2.890629823868756,4.2653756,Based on the hvgs with best normalization method,top100 +80,DAAM2,80,8714,ENSG00000146122,DAAM2,12955,5616378,74113794,True,0.6971061682850775,2.9942330891377824,4.240178,Based on the hvgs with best normalization method,top100 +81,C3_ENSG00000125730,81,5639,ENSG00000125730,C3_ENSG00000125730,11577,5625071,74572198,True,0.1863441181735022,3.2771579168293976,4.2174864,Based on the hvgs with best normalization method,top100 +82,SAMSN1,82,9748,ENSG00000155307,SAMSN1,5185,9277875,74484680,True,0.1796204277436855,3.27512934432833,4.2139206,Based on the hvgs with best normalization method,top100 +83,FA2H,83,2741,ENSG00000103089,FA2H,3279,3529192,74201872,True,0.4488143386102362,2.855949955123439,4.1875205,Based on the hvgs with best normalization method,top100 +84,CNP,84,13370,ENSG00000173786,CNP,7413,10634612,68068310,True,0.5775932053867758,2.850697087469193,4.1757283,Based on the hvgs with best normalization method,top100 +85,A2M,85,13735,ENSG00000175899,A2M,6318,9578251,74374953,True,0.3597823122995064,2.8493397666909885,4.1726813,Based on the hvgs with best normalization method,top100 +86,EYA4,86,4017,ENSG00000112319,EYA4,14674,4511586,72476380,True,0.6717625269274267,2.847782217464863,4.1691847,Based on the hvgs with best normalization method,top100 +87,SLC1A3,87,1444,ENSG00000079215,SLC1A3,21227,9582156,74406585,True,1.4390292471685913,4.344737590726089,4.157347,Based on the hvgs with best normalization method,top100 +88,PREX2,88,609,ENSG00000046889,PREX2,12132,8642053,74004383,True,1.0206908773132053,2.9471314964557203,4.137668,Based on the hvgs with best normalization method,top100 +89,CSF1R,89,14858,ENSG00000182578,CSF1R,5151,4291984,74457424,True,0.2114800007063883,3.229929765088273,4.134465,Based on the hvgs with best normalization method,top100 +90,LINC00299,90,22824,ENSG00000236790,LINC00299,23624,6051694,71833857,True,1.207794284898008,2.98642125462354,4.133465,Based on the hvgs with best normalization method,top100 +91,NDRG2,91,11540,ENSG00000165795,NDRG2,7550,9251140,74333224,True,0.484050325408196,2.826831765286455,4.122152,Based on the hvgs with best normalization method,top100 +92,PAMR1,92,9070,ENSG00000149090,PAMR1,3861,4123680,68354158,True,0.6077380143589539,2.8248041935470094,4.1176,Based on the hvgs with best normalization method,top100 +93,ADGRV1,93,11151,ENSG00000164199,ADGRV1,33822,11676625,65661938,True,1.5122817302150076,4.310319621834209,4.1089044,Based on the hvgs with best normalization method,top100 +94,FLT1,94,2679,ENSG00000102755,FLT1,12575,4248956,74491361,True,0.176550708931357,3.2082475484185897,4.0963507,Based on the hvgs with best normalization method,top100 +95,INPP5D,95,12305,ENSG00000168918,INPP5D,8681,8098619,73331347,True,0.316509087020153,3.2078499774165667,4.0956516,Based on the hvgs with best normalization method,top100 +96,RANBP3L,96,11148,ENSG00000164188,RANBP3L,4884,2795980,72610757,True,0.4411228413386047,2.8144874319792192,4.094439,Based on the hvgs with best normalization method,top100 +97,CREB5,97,8772,ENSG00000146592,CREB5,11681,13818231,74523823,True,1.0205085582340396,2.922207803928156,4.083425,Based on the hvgs with best normalization method,top100 +98,SEMA5A,98,4089,ENSG00000112902,SEMA5A,12308,9671805,74530046,True,0.9880056438061656,2.920265662984906,4.0791984,Based on the hvgs with best normalization method,top100 +99,MYRF,99,5548,ENSG00000124920,MYRF,10773,3322912,74238984,True,0.3687125871338419,2.7983769104151883,4.058272,Based on the hvgs with best normalization method,top100 +100,ITIH5,100,5317,ENSG00000123243,ITIH5,14628,2785886,73548537,True,0.0666306529037455,3.182538750378953,4.051158,Based on the hvgs with best normalization method,top150 +101,PLEKHH1,101,729,ENSG00000054690,PLEKHH1,10828,9843268,74252079,True,0.9110920306569854,2.897885880897208,4.030492,Based on the hvgs with best normalization method,top150 +102,CH25H,102,7535,ENSG00000138135,CH25H,1689,1875442,74300862,True,0.0549956938231348,3.170092250184101,4.0292783,Based on the hvgs with best normalization method,top150 +103,TBXAS1,103,794,ENSG00000059377,TBXAS1,6177,6783362,74505631,True,0.2733194386127924,3.1696655084767515,4.028528,Based on the hvgs with best normalization method,top150 +104,NPSR1-AS1,104,34011,ENSG00000197085,NPSR1-AS1,7106,1945065,43302291,True,0.4017795194522703,2.7841173350661497,4.0262594,Based on the hvgs with best normalization method,top150 +105,FLI1,105,9343,ENSG00000151702,FLI1,8026,7693942,74464122,True,0.166115425149204,3.1663141175357663,4.022637,Based on the hvgs with best normalization method,top150 +106,HIF3A,106,5470,ENSG00000124440,HIF3A,8375,6417477,73415130,True,0.5081225721678853,2.7821104305173714,4.0217543,Based on the hvgs with best normalization method,top150 +107,ADAM28,107,589,ENSG00000042980,ADAM28,9381,5065136,74357795,True,0.3184194472619878,3.1641660906533566,4.018861,Based on the hvgs with best normalization method,top150 +108,ATP10A,108,17921,ENSG00000206190,ATP10A,20675,3714316,74517624,True,0.2116144567057585,3.1628948087884106,4.016626,Based on the hvgs with best normalization method,top150 +109,SMOC1,109,17138,ENSG00000198732,SMOC1,4369,3971650,74485207,True,0.4972434499127843,2.778587589452889,4.0138454,Based on the hvgs with best normalization method,top150 +110,PLD1,110,1311,ENSG00000075651,PLD1,9954,9567983,74313755,True,0.6738888928058435,2.76621400939481,3.9860675,Based on the hvgs with best normalization method,top150 +111,DOCK1,111,9230,ENSG00000150760,DOCK1,8142,11239406,74313755,True,0.8338444532777873,2.877090504683105,3.9852338,Based on the hvgs with best normalization method,top150 +112,TMEM63A,112,16397,ENSG00000196187,TMEM63A,10350,7511569,74335350,True,0.4642410623960073,2.765740941011098,3.9850054,Based on the hvgs with best normalization method,top150 +113,CLMN,113,11581,ENSG00000165959,CLMN,15703,12091527,74394567,True,0.7772048300645735,2.873009315245747,3.9763517,Based on the hvgs with best normalization method,top150 +114,IKZF1,114,15570,ENSG00000185811,IKZF1,10921,10300033,74508828,True,0.1295384347102255,3.139404897617985,3.9753337,Based on the hvgs with best normalization method,top150 +115,CRYAB,115,3679,ENSG00000109846,CRYAB,4388,11801588,67438197,True,0.454897978900646,2.759070264912562,3.97003,Based on the hvgs with best normalization method,top150 +116,GPC5,116,14343,ENSG00000179399,GPC5,3529,11587307,71903796,True,2.03758420374263,4.469553310701985,3.9649782,Based on the hvgs with best normalization method,top150 +117,FYB1,117,1546,ENSG00000082074,FYB1,8823,13730361,66094247,True,0.2327257756583629,3.1290969404769595,3.9572136,Based on the hvgs with best normalization method,top150 +118,PLLP,118,2712,ENSG00000102934,PLLP,8705,4742334,74513630,True,0.3987087188327542,2.753008681890738,3.956422,Based on the hvgs with best normalization method,top150 +119,RUNX1,119,10201,ENSG00000159216,RUNX1,15574,14786881,74572847,True,0.2722180689884664,3.121893327708413,3.9445505,Based on the hvgs with best normalization method,top150 +120,SYK,120,11362,ENSG00000165025,SYK,5210,6875168,74511327,True,0.1801222093127282,3.1213129992938726,3.9435306,Based on the hvgs with best normalization method,top150 +121,LPAR6,121,7781,ENSG00000139679,LPAR6,4350,8865185,74360570,True,0.2398865573657181,3.120018305391312,3.9412546,Based on the hvgs with best normalization method,top150 +122,APOD,122,16278,ENSG00000189058,APOD,2022,8069876,74310190,True,0.4159060114324395,2.744119699986937,3.9364667,Based on the hvgs with best normalization method,top150 +123,MBP,123,16918,ENSG00000197971,MBP,18730,24604003,74572847,True,1.985262335530433,4.443196799991369,3.9286137,Based on the hvgs with best normalization method,top150 +124,RNF220,124,15877,ENSG00000187147,RNF220,9678,13855328,74343349,True,1.5196410359016337,4.170857519432524,3.912613,Based on the hvgs with best normalization method,top150 +125,CHI3L1,125,6628,ENSG00000133048,CHI3L1,3363,2496705,73974467,True,0.0521256280027434,3.101142113196867,3.9080725,Based on the hvgs with best normalization method,top150 +126,ACSS1,126,9713,ENSG00000154930,ACSS1,8691,6053424,74335350,True,0.4406244976018324,2.724063972952348,3.8914425,Based on the hvgs with best normalization method,top150 +127,SLC25A18,127,14925,ENSG00000182902,SLC25A18,4731,2516176,73651515,True,0.3560778227762268,2.7206031190140614,3.883673,Based on the hvgs with best normalization method,top150 +128,PLPP3,128,10595,ENSG00000162407,PLPP3,5272,9809927,65683896,True,0.523120469869442,2.7091920625504646,3.8580556,Based on the hvgs with best normalization method,top150 +129,NOS1,129,1822,ENSG00000089250,NOS1,13113,4080132,70511297,True,0.4182160889406474,2.7077747622061925,3.854874,Based on the hvgs with best normalization method,top150 +130,ATP13A4,130,5839,ENSG00000127249,ATP13A4,8988,5316409,74318981,True,0.6167409419697688,2.707156076035465,3.8534849,Based on the hvgs with best normalization method,top150 +131,SLC4A4,131,1485,ENSG00000080493,SLC4A4,9331,11765062,74511358,True,1.23618968294693,2.8581558735473145,3.8480349,Based on the hvgs with best normalization method,top150 +132,RFTN2,132,10739,ENSG00000162944,RFTN2,5776,5477177,74417288,True,0.5797464673418502,2.7036338111424296,3.8455777,Based on the hvgs with best normalization method,top150 +133,ADAMTSL1,133,14113,ENSG00000178031,ADAMTSL1,13446,6650844,69926125,True,1.286800891696848,2.854915901483216,3.8408248,Based on the hvgs with best normalization method,top150 +134,TNC,134,575,ENSG00000041982,TNC,9589,3467211,73740483,True,0.1207955697293166,3.062139409474133,3.8395107,Based on the hvgs with best normalization method,top150 +135,CCL2,135,3545,ENSG00000108691,CCL2,1935,5627111,74296150,True,0.0069795315416886,3.061338989879452,3.8381035,Based on the hvgs with best normalization method,top150 +136,TAC3,136,11805,ENSG00000166863,TAC3,1571,1114461,66776690,True,0.3986994983212539,2.6984344035745984,3.8339052,Based on the hvgs with best normalization method,top150 +137,ABCB1,137,1653,ENSG00000085563,ABCB1,6422,7351887,74484170,True,0.564911107949878,2.6978243717697468,3.8325357,Based on the hvgs with best normalization method,top150 +138,ARHGAP24,138,7613,ENSG00000138639,ARHGAP24,7870,12895378,74505631,True,1.4462750932491093,4.112176930169104,3.8300207,Based on the hvgs with best normalization method,top150 +139,MROCKI,139,20037,ENSG00000227502,MROCKI,3292,539947,69200260,True,0.07961006658828,3.0566153649362704,3.8298001,Based on the hvgs with best normalization method,top150 +140,MEGF11,140,10038,ENSG00000157890,MEGF11,9837,6040369,72280183,True,0.6653825539156258,2.69540640219595,3.8271074,Based on the hvgs with best normalization method,top150 +141,LHFPL3,141,15913,ENSG00000187416,LHFPL3,3376,10441640,62934146,True,1.8072325137962,4.367171377922477,3.82372,Based on the hvgs with best normalization method,top150 +142,ANGPTL4,142,12037,ENSG00000167772,ANGPTL4,2475,3850432,74482740,True,0.1124149674381549,3.051677377334163,3.8211198,Based on the hvgs with best normalization method,top150 +143,MERTK,143,9530,ENSG00000153208,MERTK,4133,6308017,74319885,True,0.5181886245237786,2.6861702319452694,3.8063726,Based on the hvgs with best normalization method,top150 +144,PTPRC,144,1522,ENSG00000081237,PTPRC,15436,18963917,72251824,True,0.1808028507072165,3.0428773562505222,3.8056505,Based on the hvgs with best normalization method,top150 +145,TXNIP,145,29570,ENSG00000265972,TXNIP,3604,27021024,64057359,True,0.1382867651072039,3.0428323214871136,3.8055713,Based on the hvgs with best normalization method,top150 +146,ID1,146,5713,ENSG00000125968,ID1,1233,8757100,74400727,True,0.0739092016295563,3.031862939152805,3.7862885,Based on the hvgs with best normalization method,top150 +147,CSF2RA_ENSG00000198223,147,17000,ENSG00000198223,CSF2RA_ENSG00000198223,4093,3547369,65696602,True,0.2200005198074259,3.0307859764003435,3.7843952,Based on the hvgs with best normalization method,top150 +148,KANK1,148,3361,ENSG00000107104,KANK1,25055,12851850,74564848,True,0.9146634826097708,2.7790279722435693,3.7718143,Based on the hvgs with best normalization method,top150 +149,ANLN,149,296,ENSG00000011426,ANLN,5997,4564730,74368053,True,0.3836010395319908,2.6702301478833377,3.770588,Based on the hvgs with best normalization method,top150 diff --git a/panel_design/split/8_top50.csv b/panel_design/split/8_top50.csv new file mode 100644 index 0000000000000000000000000000000000000000..d3b5c478ee1ea59a936902b3547f82eb25364dda --- /dev/null +++ b/panel_design/split/8_top50.csv @@ -0,0 +1,51 @@ +Unnamed: 0.1,Gene symbol,Unnamed: 0,soma_joinid,feature_id,feature_name,feature_length,nnz,n_measured_obs,highly_variable,means,dispersions,dispersions_norm,Annotation & Reasoning,Ranking +0,NPY,0,5241,ENSG00000122585,NPY,893,1487637,69587951,True,0.2784628391503804,5.472432619245862,8.076506,Based on the hvgs with best normalization method,top50 +1,DOCK8,1,3360,ENSG00000107099,DOCK8,20056,11548217,74513630,True,0.52630464178451,4.0060010387337766,6.769335,Based on the hvgs with best normalization method,top50 +2,APBB1IP,2,1377,ENSG00000077420,APBB1IP,3843,11013041,74541465,True,0.46146377718864,3.8968569972808864,6.524311,Based on the hvgs with best normalization method,top50 +3,OBI1-AS1,3,22073,ENSG00000234377,OBI1-AS1,10180,2521757,61741141,True,0.9734908521337996,3.958334930562165,6.3384104,Based on the hvgs with best normalization method,top50 +4,CCL3,4,32293,ENSG00000277632,CCL3,1494,3866143,61139156,True,0.0093135161911686,4.393203181327225,6.1793547,Based on the hvgs with best normalization method,top50 +5,MOBP,5,12173,ENSG00000168314,MOBP,9237,4846625,72513409,True,0.9614663545220036,3.858070407540572,6.1201987,Based on the hvgs with best normalization method,top50 +6,SPP1,6,4810,ENSG00000118785,SPP1,7250,8595089,73920484,True,0.5587869816521938,3.706557523761285,6.0970974,Based on the hvgs with best normalization method,top50 +7,PTGDS,7,3383,ENSG00000107317,PTGDS,2712,10225627,74204733,True,1.2863731638684015,3.751850425826654,5.836781,Based on the hvgs with best normalization method,top50 +8,HPSE2,8,13218,ENSG00000172987,HPSE2,4421,3904787,73047912,True,1.2129782158058935,3.7012206035381774,5.724114,Based on the hvgs with best normalization method,top50 +9,ATP1A2,9,381,ENSG00000018625,ATP1A2,6298,4848403,73460061,True,0.7347201184171539,3.663378427183657,5.696479,Based on the hvgs with best normalization method,top50 +10,VCAN,10,548,ENSG00000038427,VCAN,14678,12407214,74552031,True,0.7631149795408386,3.600036576582366,5.5586243,Based on the hvgs with best normalization method,top50 +11,HSPA1A,11,17512,ENSG00000204389,HSPA1A,2404,20330619,64819739,True,0.3483590391423253,3.464540264521311,5.553779,Based on the hvgs with best normalization method,top50 +12,HTR2C,12,8855,ENSG00000147246,HTR2C,4842,3271887,58827748,True,0.7858794209017294,3.5926348724054007,5.5425153,Based on the hvgs with best normalization method,top50 +13,LINC00499,13,25247,ENSG00000251372,LINC00499,20131,1680795,57253942,True,0.5150609102577905,3.4490050961280407,5.5189033,Based on the hvgs with best normalization method,top50 +14,CERCAM,14,11877,ENSG00000167123,CERCAM,6058,6253645,74320849,True,0.7828653201335859,3.565729125607589,5.4839587,Based on the hvgs with best normalization method,top50 +15,P2RY12,15,12400,ENSG00000169313,P2RY12,2337,2640423,71547277,True,0.4689977371721238,3.4120002061266947,5.435829,Based on the hvgs with best normalization method,top50 +16,GFAP,16,6331,ENSG00000131095,GFAP,11229,2603965,73350833,True,0.2876280341852898,3.936925670728887,5.377276,Based on the hvgs with best normalization method,top50 +17,SLC5A11,17,10161,ENSG00000158865,SLC5A11,3415,3364055,69923585,True,0.574600980890718,3.376484619946382,5.356098,Based on the hvgs with best normalization method,top50 +18,CNDP1,18,9219,ENSG00000150656,CNDP1,7117,3107345,74297237,True,0.5260503757110235,3.324706229330675,5.239858,Based on the hvgs with best normalization method,top50 +19,XIST,19,20711,ENSG00000229807,XIST,25266,21056286,74315539,True,1.1813301289979623,3.466603367504034,5.2020183,Based on the hvgs with best normalization method,top50 +20,TMEM144,20,11123,ENSG00000164124,TMEM144,9248,8045941,74437632,True,0.9652175529364386,3.4310867041448723,5.1909285,Based on the hvgs with best normalization method,top50 +21,CCL4,21,31901,ENSG00000275302,CCL4,1795,6143173,62760344,True,0.0025910273861521,3.8228119051090586,5.176678,Based on the hvgs with best normalization method,top50 +22,CLDN5,22,15192,ENSG00000184113,CLDN5,3429,2524056,74446360,True,0.0437600914665603,3.819325395509967,5.1705494,Based on the hvgs with best normalization method,top50 +23,LINC01170,23,25766,ENSG00000253807,LINC01170,3850,3703423,68171161,True,0.5707032423248326,3.285253035209508,5.151287,Based on the hvgs with best normalization method,top50 +24,HERC2P3_ENSG00000180229,24,33730,ENSG00000180229,HERC2P3_ENSG00000180229,4088,1827785,27752448,True,0.2803260141440332,3.767946887391629,5.080232,Based on the hvgs with best normalization method,top50 +25,COL5A3,25,1491,ENSG00000080573,COL5A3,6783,5447597,74449798,True,0.7555858023716054,3.378410803350078,5.076287,Based on the hvgs with best normalization method,top50 +26,C10orf90,26,9666,ENSG00000154493,C10orf90,5659,4440265,67152709,True,0.875581664155515,3.374201105219235,5.0671253,Based on the hvgs with best normalization method,top50 +27,VIP,27,8761,ENSG00000146469,VIP,1585,1138785,68691606,True,0.7213746131758096,3.3694517010030616,5.056789,Based on the hvgs with best normalization method,top50 +28,DOCK5,28,8882,ENSG00000147459,DOCK5,15989,10823963,74290291,True,0.9082107891458808,3.368977884127573,5.0557575,Based on the hvgs with best normalization method,top50 +29,LINC00609,29,27091,ENSG00000257585,LINC00609,583,4992807,50838830,True,0.7328977593171419,3.348563758499239,5.011329,Based on the hvgs with best normalization method,top50 +30,ENPP2,30,7309,ENSG00000136960,ENPP2,6729,6966932,74560519,True,0.7944294216609206,3.333032659221951,4.9775276,Based on the hvgs with best normalization method,top50 +31,THSD7B,31,8449,ENSG00000144229,THSD7B,6708,6341082,70419221,True,0.9194696182982336,3.310702524025904,4.9289293,Based on the hvgs with best normalization method,top50 +32,KCNH8,32,15168,ENSG00000183960,KCNH8,6088,6157298,74266159,True,0.883848466775706,3.3051588757014865,4.9168644,Based on the hvgs with best normalization method,top50 +33,CST3,33,2510,ENSG00000101439,CST3,3615,27512197,74668992,True,0.6748217603618987,3.1795989384367074,4.9140983,Based on the hvgs with best normalization method,top50 +34,EBF1,34,11191,ENSG00000164330,EBF1,6316,6862033,74452348,True,0.0808396016860488,3.669852728684886,4.907795,Based on the hvgs with best normalization method,top50 +35,ZFP36L1,35,15531,ENSG00000185650,ZFP36L1,6466,26391142,74394567,True,0.3487557774474227,3.173621365905224,4.900679,Based on the hvgs with best normalization method,top50 +36,FAM177B,36,16783,ENSG00000197520,FAM177B,2805,1011468,74266705,True,0.1702217044119632,3.648502115335857,4.870263,Based on the hvgs with best normalization method,top50 +37,SST,37,9936,ENSG00000157005,SST,607,2166462,63111103,True,0.794279151054743,3.2809449870199447,4.8641663,Based on the hvgs with best normalization method,top50 +38,MOG,38,17608,ENSG00000204655,MOG,3175,2662310,63168628,True,0.5889720942517631,3.155045946295618,4.8589783,Based on the hvgs with best normalization method,top50 +39,ID3,39,4659,ENSG00000117318,ID3,1496,10773972,72735199,True,0.11467277785575,3.636231982980021,4.848694,Based on the hvgs with best normalization method,top50 +40,CARNS1,40,13126,ENSG00000172508,CARNS1,5670,2858746,74329638,True,0.4607257690794789,3.13743267495587,4.819437,Based on the hvgs with best normalization method,top50 +41,NHSL1,41,7026,ENSG00000135540,NHSL1,8741,8516367,74564094,True,0.6323054715980764,3.1336152777375865,4.8108673,Based on the hvgs with best normalization method,top50 +42,SLC1A2,42,3748,ENSG00000110436,SLC1A2,22800,14723654,74245583,True,2.2372810686961864,5.118459519668878,4.8069806,Based on the hvgs with best normalization method,top50 +43,SCGB1B2P,43,30347,ENSG00000268751,SCGB1B2P,754,513073,71291662,True,0.0023219452044399,3.596334101249868,4.7785583,Based on the hvgs with best normalization method,top50 +44,OLIG1,44,15219,ENSG00000184221,OLIG1,2273,2770870,73073562,True,0.3798056667882042,3.103276777107508,4.7427588,Based on the hvgs with best normalization method,top50 +45,UGT8,45,13504,ENSG00000174607,UGT8,4385,4502125,74250259,True,0.5682002703328997,3.0803347057375188,4.6912546,Based on the hvgs with best normalization method,top50 +46,OPALIN,46,16760,ENSG00000197430,OPALIN,3874,1835848,56197923,True,0.4221487818214699,3.0707599776788013,4.6697598,Based on the hvgs with best normalization method,top50 +47,FRMD4B,47,4266,ENSG00000114541,FRMD4B,11201,16560570,74505631,True,1.222521920193563,3.2186813547572046,4.6503153,Based on the hvgs with best normalization method,top50 +48,CLDN11,48,327,ENSG00000013297,CLDN11,4321,3987554,72517586,True,0.5142272418796419,3.052022016031001,4.6276937,Based on the hvgs with best normalization method,top50 +49,MAG,49,3147,ENSG00000105695,MAG,2960,2791162,73743438,True,0.4186199035688159,3.0412205225704634,4.603445,Based on the hvgs with best normalization method,top50 diff --git a/panel_design/split/9_top100.csv b/panel_design/split/9_top100.csv new file mode 100644 index 0000000000000000000000000000000000000000..0d091a4deb3b2e908f0a8d9d5a74db5acde3a3df --- /dev/null +++ b/panel_design/split/9_top100.csv @@ -0,0 +1,102 @@ +Unnamed: 0,Gene symbol,Ranking,Annotation & reasoning,Additional note,Paper links,Gene Symbol +1,SNAP25,1.0,Regional and laminal marker : Gray matter ,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,SNAP25 +2,MBP,2.0,Regional and laminal marker : White matter ,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,MBP +3,PCP4,3.0,Regional and laminal marker : L5 ,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,PCP4 +4,RELN,4.0,Regional and laminal marker : L1 / Gabaergic neuron subclass: LAMP5/RELN/LHX7,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,RELN +5,NR4A2,5.0,Regional and laminal marker : L6 ,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,NR4A2 +6,HTRA1,6.0,Regional and laminal marker : L1 sublayer,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,HTRA1 +7,SPARC,7.0,Regional and laminal marker : L1 sublayer,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,SPARC +8,CLDN5,8.0,Brain vasculature/endothelial cell marker,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,CLDN5 +9,AQP4,9.0,Regional and laminal marker : L1 /Astrocyte marker,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,AQP4 +10,NeuN,10.0,Neuronal marker,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials,NeuN +11,INA,11.0,Neuronal marker,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials,INA +12,SLC17A6,12.0,Glutamergic neuron marker,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials,SLC17A6 +13,SLC17A7,13.0,Glutamergic neuron marker,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials,SLC17A7 +14,SLC32A1,14.0,Gabaergic neuron marker ,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials,SLC32A1 +15,PTRPC,15.0,Immune cell marker,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,PTRPC +16,ACTA2,16.0,Smooth muscle cell,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,ACTA2 +17,CEMIP,17.0,VCMC,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,CEMIP +18,PCDH8,18.0,Glutamergic neuron subclass: L3-3 IT ,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,PCDH8 +19,OPRK1,19.0,Glutamergic neuron subclass: L6-IT 1/2 Glut,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,OPRK1 +20,RORB,20.0,Glutamergic neuron subclass: L3-5IT 1/2/3 Glut,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,RORB +21,FEZF2,21.0,Glutamergic neuron subclass: L5ET,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,FEZF2 +22,HTR2C,22.0,Glutamergic neuron subclass: L5-6 NP,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,HTR2C +23,SYT6,23.0,Glutamergic neuron subclass: L6 CT,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,SYT6 +24,CTGF,24.0,Glutamergic neuron subclass: L6 B,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,CTGF +25,LAMP5,25.0,Gabaergic neuron subclass: LAMP5/RELN/LHX6,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,LAMP5 +26,LHX6,26.0,Gabaergic neuron subclass: LAMP5/RELN/LHX8,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,LHX6 +27,VIP,27.0,Gabaergic neuron subclass VIP,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,VIP +28,KCNG1,28.0,Gabaergic neuron subclass VIP KCNG1,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,KCNG1 +29,SST,29.0,Gabaergic neuron subclass SST,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,SST +30,HGF,30.0,Gabaergic neuron subclass SST HGF,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,HGF +31,PVALB,31.0,Gabaergic neuron subclass SST PVALB,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,PVALB +32,CHC,32.0,Gabaergic neuron subclass SST PVALB CHC,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,CHC +33,FABP7,33.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,FABP7 +34,AQP1,34.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,AQP1 +35,SLC1A2,35.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,SLC1A2 +36,GFAP,36.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,GFAP +37,OSMR,37.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,OSMR +38,PDGFRA,38.0,"Non neuronal subclass, OPC and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,PDGFRA +39,PCDH15,39.0,"Non neuronal subclass, OPC and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,PCDH15 +40,MOG,40.0,"Non neuronal subclass, Oligodendrocytes and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,MOG +41,CDH7,41.0,"Non neuronal subclass, Oligodendrocytes and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,CDH7 +42,OPALIN,42.0,"Non neuronal subclass, Oligodendrocytes and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,OPALIN +43,GSN,43.0,"Non neuronal subclass, Oligodendrocytes and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,GSN +45,P2RY12,44.0,"Non neuronal subclass, microglia and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,P2RY12 +46,IGKC,45.0,"Immune cell, B cell ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,IGKC +47,CD247,46.0,"Immune cell, T cell ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,CD247 +48,COLEC12,47.0,"Immune cell, Macrophage","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,COLEC12 +50,FOS,48.0,Neuronal activity gene - cFos,"Aparicio et al., 2022 - Current Opinion on the Use of c-Fos in Neuroscience",https://www.mdpi.com/2673-4087/3/4/50,FOS +51,CALM1,49.0,Neuronal activity gene - Calmodulin 1,"Jensen et al., 2024 - Neurological consequences of human calmodulin mutations +",https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10749624/,CALM1 +52,APBB7IP,50.0,"Non neuronal subclass, microglia and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",,APBB7IP +54,NRXN3,51.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,NRXN3 +55,SYN1,52.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,SYN1 +56,SYN2,53.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,SYN2 +57,SYN3,54.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,SYN3 +58,SYP,55.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,SYP +59,SYT1,56.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,SYT1 +60,STX1A,57.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,STX1A +61,VAMP2,58.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,VAMP2 +62,VGAT,59.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,VGAT +63,VGLUT1,60.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,VGLUT1 +64,VGLUT2,61.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,VGLUT2 +65,VGLUT3,62.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,VGLUT3 +66,GAP43,63.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,GAP43 +67,VMAT2,64.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,VMAT2 +68,NRG1,65.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,NRG1 +69,DLG4,66.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,DLG4 +70,DLG3,67.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,DLG3 +71,SHANK1,68.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,SHANK1 +72,SHANK3,69.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,SHANK3 +73,HOMER1,70.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,HOMER1 +74,HOMER2,71.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,HOMER2 +75,HOMER3,72.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,HOMER3 +76,GPHN,73.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,GPHN +77,ICAM1,74.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5199,ICAM1 +78,AKT1,75.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5200,AKT1 +79,MECP2,76.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5201,MECP2 +80,PTK2B,77.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5202,PTK2B +81,EPHA2,78.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5203,EPHA2 +82,RARG,79.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5204,RARG +83,PML,80.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5205,PML +84,EPB41,81.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5206,EPB41 +85,DMD,82.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5207,DMD +86,FOXO1,83.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5208,FOXO1 +87,TEK,84.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5209,TEK +88,CDH5,85.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5210,CDH5 +89,COL3A1,86.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5211,COL3A1 +90,HIST1HE,87.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5212,HIST1HE +91,PRKDC,88.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5213,PRKDC +92,HMGB1,89.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5214,HMGB1 +93,HMGB2,90.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5215,HMGB2 +94,PDGFB,91.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5216,PDGFB +95,CRLF1,92.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5217,CRLF1 +96,NAMPT,93.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5218,NAMPT +97,ANGPT1,94.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5219,ANGPT1 +98,CXCL12,95.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5220,CXCL12 +99,ANGPT2,96.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5221,ANGPT2 +100,PIK3CB,97.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5222,PIK3CB +101,SEMA5A,98.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5223,SEMA5A +103,ZNF263,99.0,Transcription factor linked to GRN from prefrontal cortex (PFC) - Broad cell types ,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5199,ZNF263 +104,MAZ,100.0,Transcription factor linked to GRN from prefrontal cortex (PFC) - Broad cell types ,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5200,MAZ diff --git a/panel_design/split/9_top150.csv b/panel_design/split/9_top150.csv new file mode 100644 index 0000000000000000000000000000000000000000..adbd167aaf9710266c0721487846c6ee8351218e --- /dev/null +++ b/panel_design/split/9_top150.csv @@ -0,0 +1,152 @@ +Unnamed: 0,Gene symbol,Ranking,Annotation & reasoning,Additional note,Paper links,Gene Symbol +1,SNAP25,1.0,Regional and laminal marker : Gray matter ,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,SNAP25 +2,MBP,2.0,Regional and laminal marker : White matter ,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,MBP +3,PCP4,3.0,Regional and laminal marker : L5 ,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,PCP4 +4,RELN,4.0,Regional and laminal marker : L1 / Gabaergic neuron subclass: LAMP5/RELN/LHX7,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,RELN +5,NR4A2,5.0,Regional and laminal marker : L6 ,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,NR4A2 +6,HTRA1,6.0,Regional and laminal marker : L1 sublayer,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,HTRA1 +7,SPARC,7.0,Regional and laminal marker : L1 sublayer,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,SPARC +8,CLDN5,8.0,Brain vasculature/endothelial cell marker,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,CLDN5 +9,AQP4,9.0,Regional and laminal marker : L1 /Astrocyte marker,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,AQP4 +10,NeuN,10.0,Neuronal marker,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials,NeuN +11,INA,11.0,Neuronal marker,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials,INA +12,SLC17A6,12.0,Glutamergic neuron marker,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials,SLC17A6 +13,SLC17A7,13.0,Glutamergic neuron marker,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials,SLC17A7 +14,SLC32A1,14.0,Gabaergic neuron marker ,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials,SLC32A1 +15,PTRPC,15.0,Immune cell marker,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,PTRPC +16,ACTA2,16.0,Smooth muscle cell,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,ACTA2 +17,CEMIP,17.0,VCMC,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,CEMIP +18,PCDH8,18.0,Glutamergic neuron subclass: L3-3 IT ,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,PCDH8 +19,OPRK1,19.0,Glutamergic neuron subclass: L6-IT 1/2 Glut,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,OPRK1 +20,RORB,20.0,Glutamergic neuron subclass: L3-5IT 1/2/3 Glut,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,RORB +21,FEZF2,21.0,Glutamergic neuron subclass: L5ET,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,FEZF2 +22,HTR2C,22.0,Glutamergic neuron subclass: L5-6 NP,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,HTR2C +23,SYT6,23.0,Glutamergic neuron subclass: L6 CT,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,SYT6 +24,CTGF,24.0,Glutamergic neuron subclass: L6 B,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,CTGF +25,LAMP5,25.0,Gabaergic neuron subclass: LAMP5/RELN/LHX6,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,LAMP5 +26,LHX6,26.0,Gabaergic neuron subclass: LAMP5/RELN/LHX8,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,LHX6 +27,VIP,27.0,Gabaergic neuron subclass VIP,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,VIP +28,KCNG1,28.0,Gabaergic neuron subclass VIP KCNG1,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,KCNG1 +29,SST,29.0,Gabaergic neuron subclass SST,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,SST +30,HGF,30.0,Gabaergic neuron subclass SST HGF,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,HGF +31,PVALB,31.0,Gabaergic neuron subclass SST PVALB,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,PVALB +32,CHC,32.0,Gabaergic neuron subclass SST PVALB CHC,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,CHC +33,FABP7,33.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,FABP7 +34,AQP1,34.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,AQP1 +35,SLC1A2,35.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,SLC1A2 +36,GFAP,36.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,GFAP +37,OSMR,37.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,OSMR +38,PDGFRA,38.0,"Non neuronal subclass, OPC and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,PDGFRA +39,PCDH15,39.0,"Non neuronal subclass, OPC and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,PCDH15 +40,MOG,40.0,"Non neuronal subclass, Oligodendrocytes and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,MOG +41,CDH7,41.0,"Non neuronal subclass, Oligodendrocytes and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,CDH7 +42,OPALIN,42.0,"Non neuronal subclass, Oligodendrocytes and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,OPALIN +43,GSN,43.0,"Non neuronal subclass, Oligodendrocytes and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,GSN +45,P2RY12,44.0,"Non neuronal subclass, microglia and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,P2RY12 +46,IGKC,45.0,"Immune cell, B cell ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,IGKC +47,CD247,46.0,"Immune cell, T cell ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,CD247 +48,COLEC12,47.0,"Immune cell, Macrophage","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,COLEC12 +50,FOS,48.0,Neuronal activity gene - cFos,"Aparicio et al., 2022 - Current Opinion on the Use of c-Fos in Neuroscience",https://www.mdpi.com/2673-4087/3/4/50,FOS +51,CALM1,49.0,Neuronal activity gene - Calmodulin 1,"Jensen et al., 2024 - Neurological consequences of human calmodulin mutations +",https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10749624/,CALM1 +52,APBB7IP,50.0,"Non neuronal subclass, microglia and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",,APBB7IP +54,NRXN3,51.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,NRXN3 +55,SYN1,52.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,SYN1 +56,SYN2,53.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,SYN2 +57,SYN3,54.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,SYN3 +58,SYP,55.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,SYP +59,SYT1,56.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,SYT1 +60,STX1A,57.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,STX1A +61,VAMP2,58.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,VAMP2 +62,VGAT,59.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,VGAT +63,VGLUT1,60.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,VGLUT1 +64,VGLUT2,61.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,VGLUT2 +65,VGLUT3,62.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,VGLUT3 +66,GAP43,63.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,GAP43 +67,VMAT2,64.0,Pre synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,VMAT2 +68,NRG1,65.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,NRG1 +69,DLG4,66.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,DLG4 +70,DLG3,67.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,DLG3 +71,SHANK1,68.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,SHANK1 +72,SHANK3,69.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,SHANK3 +73,HOMER1,70.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,HOMER1 +74,HOMER2,71.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,HOMER2 +75,HOMER3,72.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,HOMER3 +76,GPHN,73.0,Post synaptic marker,https://www.alomone.com/synaptic-markers-for-pre-and-postsynaptic-regions?srsltid=AfmBOorvpugcJXthma_V9UY2fua_gCkNcxyRF6fHMPELLMW3HA5V5iv0#Presynaptic-Markers,,GPHN +77,ICAM1,74.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5199,ICAM1 +78,AKT1,75.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5200,AKT1 +79,MECP2,76.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5201,MECP2 +80,PTK2B,77.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5202,PTK2B +81,EPHA2,78.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5203,EPHA2 +82,RARG,79.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5204,RARG +83,PML,80.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5205,PML +84,EPB41,81.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5206,EPB41 +85,DMD,82.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5207,DMD +86,FOXO1,83.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5208,FOXO1 +87,TEK,84.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5209,TEK +88,CDH5,85.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5210,CDH5 +89,COL3A1,86.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5211,COL3A1 +90,HIST1HE,87.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5212,HIST1HE +91,PRKDC,88.0,Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5213,PRKDC +92,HMGB1,89.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5214,HMGB1 +93,HMGB2,90.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5215,HMGB2 +94,PDGFB,91.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5216,PDGFB +95,CRLF1,92.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5217,CRLF1 +96,NAMPT,93.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5218,NAMPT +97,ANGPT1,94.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5219,ANGPT1 +98,CXCL12,95.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5220,CXCL12 +99,ANGPT2,96.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5221,ANGPT2 +100,PIK3CB,97.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5222,PIK3CB +101,SEMA5A,98.0,Interacting ligands on non neuronal cells of Schizophrenia risk gene receptors,"Ligand-receptor pairs Schizophrenia risk - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5223,SEMA5A +103,ZNF263,99.0,Transcription factor linked to GRN from prefrontal cortex (PFC) - Broad cell types ,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5199,ZNF263 +104,MAZ,100.0,Transcription factor linked to GRN from prefrontal cortex (PFC) - Broad cell types ,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5200,MAZ +105,ZNF148,101.0,Transcription factor linked to GRN from prefrontal cortex (PFC) - Broad cell types ,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5201,ZNF148 +106,MEF2C,102.0,Transcription factor linked to GRN from prefrontal cortex (PFC) - Broad cell types ,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5202,MEF2C +107,SP2,103.0,Transcription factor linked to GRN from prefrontal cortex (PFC) - Broad cell types ,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5203,SP2 +108,ZEB1,104.0,Transcription factor linked to GRN from prefrontal cortext (PFC) - more cell type specific,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5204,ZEB1 +109,PU2F2,105.0,Transcription factor linked to GRN from prefrontal cortext (PFC) - more cell type specific,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5205,PU2F2 +110,PPARA,106.0,Transcription factor linked to GRN from prefrontal cortext (PFC) - more cell type specific,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5206,PPARA +111,PBX3,107.0,Transcription factor linked to GRN from prefrontal cortext (PFC) - more cell type specific,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5207,PBX3 +112,ELK4,108.0,Transcription factor linked to GRN from prefrontal cortext (PFC) - more cell type specific,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5208,ELK4 +113,ETV6,109.0,Transcription factor linked to GRN from prefrontal cortext (PFC) - more cell type specific,"GRNs and TFs - Emani et al., 2024, Single-cell genomics and regulatory networks for 388 human brains",https://www.science.org/doi/10.1126/science.adi5209,ETV6 +114,CLCN3,110.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",CLCN3 +115,CNTN4,111.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",CNTN4 +116,GATAD2A,112.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",GATAD2A +117,GPM6A,113.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",GPM6A +118,MMP16,114.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",MMP16 +119,PSMA4,115.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",PSMA4 +120,TCF4,116.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",TCF4 +121,NCAN,117.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",NCAN +122,MAPK3,118.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",MAPK3 +123,NMRAL1,119.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",NMRAL1 +124,CHRNB4,120.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",CHRNB4 +125,CHRNA3,121.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",CHRNA3 +126,CHRNA5,122.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",CHRNA5 +127,IREB2,123.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",IREB2 +128,PPP1R13B,124.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",PPP1R13B +129,BCL11B,125.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",BCL11B +130,PRKD1,126.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",PRKD1 +131,OGFOD2,127.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",OGFOD2 +132,ATP2A2,128.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",ATP2A2 +133,SNX19,129.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",SNX19 +134,NRGN,130.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",NRGN +135,DRD2,131.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",DRD2 +136,SERPING1,132.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",SERPING1 +137,ZDHHC5,133.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",ZDHHC5 +138,CACNB2,134.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",CACNB2 +139,KCNV1,135.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",KCNV1 +140,NNM16,136.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",NNM16 +141,SNAP91,137.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",SNAP91 +142,GRIA1,138.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",GRIA1 +143,PCDHA5,139.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",PCDHA5 +144,PCDHA8,140.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",PCDHA8 +145,HCN1,141.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",HCN1 +146,CLCN3,142.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",CLCN3 +147,TMEM22,143.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",TMEM22 +148,NEK4,144.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",NEK4 +149,PBRM1,145.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",PBRM1 +150,ALMS1,146.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",ALMS1 +151,VRK2,147.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",VRK2 +152,DUS2L,148.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",DUS2L +153,FURIN,149.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",FURIN +154,GRIN2A,150.0,Predicted causal Schizophrenia genes,"Predicted causal schizophrenia genes - Ma et al., 2018 - The integrated landscape of causal genes and pathways in schizophrenia","https://www.nature.com/articles/s41398-018-0114-x#:~:text=TCF4%20is%20one%20of%20the,a%20causal%20gene%20for%20schizophrenia.",GRIN2A diff --git a/panel_design/split/9_top50.csv b/panel_design/split/9_top50.csv new file mode 100644 index 0000000000000000000000000000000000000000..78d33dea260a592bd91be3277a21cee538638b0d --- /dev/null +++ b/panel_design/split/9_top50.csv @@ -0,0 +1,52 @@ +Unnamed: 0,Gene symbol,Ranking,Annotation & reasoning,Additional note,Paper links,Gene Symbol +1,SNAP25,1.0,Regional and laminal marker : Gray matter ,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,SNAP25 +2,MBP,2.0,Regional and laminal marker : White matter ,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,MBP +3,PCP4,3.0,Regional and laminal marker : L5 ,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,PCP4 +4,RELN,4.0,Regional and laminal marker : L1 / Gabaergic neuron subclass: LAMP5/RELN/LHX7,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,RELN +5,NR4A2,5.0,Regional and laminal marker : L6 ,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,NR4A2 +6,HTRA1,6.0,Regional and laminal marker : L1 sublayer,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,HTRA1 +7,SPARC,7.0,Regional and laminal marker : L1 sublayer,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,SPARC +8,CLDN5,8.0,Brain vasculature/endothelial cell marker,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,CLDN5 +9,AQP4,9.0,Regional and laminal marker : L1 /Astrocyte marker,"Region annotation: Maynard lab - Huuki-Myers et al., 2024 - A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex",https://www.science.org/doi/10.1126/science.adh1938?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,AQP4 +10,NeuN,10.0,Neuronal marker,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials,NeuN +11,INA,11.0,Neuronal marker,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials,INA +12,SLC17A6,12.0,Glutamergic neuron marker,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials,SLC17A6 +13,SLC17A7,13.0,Glutamergic neuron marker,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials,SLC17A7 +14,SLC32A1,14.0,Gabaergic neuron marker ,"Key neuron parent annotation: Linnarson lab - Siletti et al., 2023 - Transcriptomic diversity of cell types across the adult human brain",https://www.science.org/doi/10.1126/science.add7046#supplementary-materials,SLC32A1 +15,PTRPC,15.0,Immune cell marker,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,PTRPC +16,ACTA2,16.0,Smooth muscle cell,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,ACTA2 +17,CEMIP,17.0,VCMC,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,CEMIP +18,PCDH8,18.0,Glutamergic neuron subclass: L3-3 IT ,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,PCDH8 +19,OPRK1,19.0,Glutamergic neuron subclass: L6-IT 1/2 Glut,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,OPRK1 +20,RORB,20.0,Glutamergic neuron subclass: L3-5IT 1/2/3 Glut,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,RORB +21,FEZF2,21.0,Glutamergic neuron subclass: L5ET,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,FEZF2 +22,HTR2C,22.0,Glutamergic neuron subclass: L5-6 NP,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,HTR2C +23,SYT6,23.0,Glutamergic neuron subclass: L6 CT,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,SYT6 +24,CTGF,24.0,Glutamergic neuron subclass: L6 B,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,CTGF +25,LAMP5,25.0,Gabaergic neuron subclass: LAMP5/RELN/LHX6,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,LAMP5 +26,LHX6,26.0,Gabaergic neuron subclass: LAMP5/RELN/LHX8,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,LHX6 +27,VIP,27.0,Gabaergic neuron subclass VIP,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,VIP +28,KCNG1,28.0,Gabaergic neuron subclass VIP KCNG1,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,KCNG1 +29,SST,29.0,Gabaergic neuron subclass SST,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,SST +30,HGF,30.0,Gabaergic neuron subclass SST HGF,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,HGF +31,PVALB,31.0,Gabaergic neuron subclass SST PVALB,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,PVALB +32,CHC,32.0,Gabaergic neuron subclass SST PVALB CHC,"Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,CHC +33,FABP7,33.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,FABP7 +34,AQP1,34.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,AQP1 +35,SLC1A2,35.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,SLC1A2 +36,GFAP,36.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,GFAP +37,OSMR,37.0,"Non neuronal subclass, Astrocytes and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,OSMR +38,PDGFRA,38.0,"Non neuronal subclass, OPC and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,PDGFRA +39,PCDH15,39.0,"Non neuronal subclass, OPC and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,PCDH15 +40,MOG,40.0,"Non neuronal subclass, Oligodendrocytes and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,MOG +41,CDH7,41.0,"Non neuronal subclass, Oligodendrocytes and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,CDH7 +42,OPALIN,42.0,"Non neuronal subclass, Oligodendrocytes and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,OPALIN +43,GSN,43.0,"Non neuronal subclass, Oligodendrocytes and subtypes","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,GSN +45,P2RY12,44.0,"Non neuronal subclass, microglia and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,P2RY12 +46,IGKC,45.0,"Immune cell, B cell ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,IGKC +47,CD247,46.0,"Immune cell, T cell ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,CD247 +48,COLEC12,47.0,"Immune cell, Macrophage","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",https://www.science.org/doi/10.1126/science.abo7257#supplementary-materials,COLEC12 +50,FOS,48.0,Neuronal activity gene - cFos,"Aparicio et al., 2022 - Current Opinion on the Use of c-Fos in Neuroscience",https://www.mdpi.com/2673-4087/3/4/50,FOS +51,CALM1,49.0,Neuronal activity gene - Calmodulin 1,"Jensen et al., 2024 - Neurological consequences of human calmodulin mutations +",https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10749624/,CALM1 +52,APBB7IP,50.0,"Non neuronal subclass, microglia and subtypes ","Neuronal subclass, non-neuronal subtypes annotation: Sheston lab - Ma et al., 2022 - Molecular and cellular evolution of the primate dorsolateral prefrontal cortex",,APBB7IP diff --git a/panel_design/workflows.csv b/panel_design/workflows.csv new file mode 100644 index 0000000000000000000000000000000000000000..bc4c824ad8988b57b90e062ba2a6a733fcde358c --- /dev/null +++ b/panel_design/workflows.csv @@ -0,0 +1,11 @@ +id,workflow +1,"Literature-based approach with an integrated human PFC atlas; selected cell-type markers from literature and added markers for neuronal activity and neuropathologies (Alzheimer's, Schizophrenia)." +2,"Reference dataset only; applied the Persist algorithm to select top 50/100/150 genes, algorithmic without manual curation." +3,Submitted a previously designed panel (incorrect tissue). +4,"Combined reference dataset with literature; gene enrichment ranked with Cohen's mean, supplemented with known marker genes for key cell types." +5,"Reference dataset only; iterative greedy algorithm for kNN-graph reconstruction, focused on a graph-based representation of the data." +6,Alternative dataset (not the provided one); selected genes to differentiate predicted cell types by integrating scRNA-seq with Visium data and spot-level deconvolution. +7,Multiple datasets (provided + cell-type-specific studies); cell-type markers from multiple references plus DE genes; included genes targeting intracellular pathways and cross-referenced markers across studies. +8,Reference dataset only; selected highly variable genes with optimal normalization techniques. +9,"Literature only; cell-type markers from broad and targeted studies, including presynaptic markers, transcription factors, and Schizophrenia risk genes in GRNs." +10,"Reference dataset only; selected top DE genes per cell type, focused on differential expression."