| # 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). |
|
|
