sc-cell-annotation

sc-cell-annotation

When to use

The user has a clustered AnnData (e.g. obs["leiden"]) and wants labelled cell types in obs["cell_type"]. Pick a method by data / reference availability:

• markers (default) — built-in or custom marker-gene scoring.
• manual — user-supplied cluster-to-label map.
• celltypist — pretrained Immune_All_Low.pkl style classifier.
• popv / knnpredict — reference AnnData mapping (PopV consensus or lightweight KNN).
• singler / scmap — R-backed reference annotation.
• scsa — Fisher-test DB scoring (species, tissue).

This skill labels — for ranking the genes that justify a label use sc-markers; for replicate-aware condition DE use sc-de.

Use from a step

annotation = load_skill("sc-cell-annotation")
adata = read_input("results/03_clustering/intermediate/adata_clustered.h5ad")
# celltypist Immune_All_Low: PBMC sample; the model covers circulating immune types.
adata = annotation.annotate(adata, method="celltypist", model="Immune_All_Low")
run = annotation.run_info(adata)
assert run["summary"]["actual_method"] == "celltypist", run["summary"]["fallback_reason"]
write_output(annotation.annotation_table(adata), "tables/cell_type_counts.csv")
write_output(annotation.annotation_figure(adata), "figures/umap_cell_type.png")
write_output(adata, "intermediate/adata_annotated.h5ad")

A complete step that runs on demo data: examples/example_step.py.

API

annotate(adata, *, method: str='markers', cluster_key: str | None=None, markers: dict[str, list[str]] | None=None, marker_file: str | None=None, model: str='Immune_All_Low', majority_voting: bool=False, reference: str='HPCA', manual_map: str | None=None, manual_map_file: str | None=None, species: str='Human', tissue: str='All', scsa_foldchange: float=1.5, scsa_pvalue: float=0.05)

Annotate cell types with the chosen method; the single entry point to the method functions.