Add files using upload-large-folder tool

.gitattributes

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 # 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

README.md

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+---
+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<n<1M
+configs:
+- config_name: panel_workflows
+  data_files: panel_design/workflows.csv
+- config_name: annotation_workflows
+  data_files: annotation/workflows.csv
+---
+
+# SpatialAgent — Human Expert Reference Data
+
+Anonymized reference data produced by human scientists for two spatial-transcriptomics
+tasks used to benchmark **SpatialAgent**:
+
+1. **Gene panel design** — expert-designed targeted gene panels for the human
+   **dorsolateral prefrontal cortex (DLPFC / PFC)**.
+2. **Cell-type & tissue-niche annotation** — expert annotations of a **developing human
+   heart** MERFISH dataset (228,633 cells × 238 genes).
+
+All scientist identities are removed. Each task uses its **own independent numbering**, so
+the same person generally has a *different* id in the two tasks (this is intentional — the
+two studies were anonymized separately). No real names appear anywhere in this repository.
+Each expert's methodology is documented (by anonymized id) in the `workflows.csv` files.
+
+## Repository layout
+
+```
+panel_design/
+  workflows.csv              # id (1–10) -> 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.

annotation/README.md

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+# 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).

annotation/per_expert_raw/expert2.h5ad

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+size 362532421

annotation/per_expert_raw/expert3.csv

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annotation/per_expert_raw/expert4.csv

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annotation/per_expert_raw/expert5.h5ad

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annotation/per_expert_raw/expert7.h5ad

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+oid sha256:55875bd147ae95013cfea2cc017e0c7ed8f702b67ad4ee9438d25365c4e00390
+size 357686236

annotation/workflows.csv

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+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.

panel_design/1.csv

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+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,

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,

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",

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,,

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

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

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

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

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

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."

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.

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",

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,

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,

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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