cellink.tl.external.compute_diseaseprogression_programs

cellink.tl.external.compute_diseaseprogression_programs(adata, celltype_col, diagnosis_col, healthy_label, disease_label, *, min_cells_per_group=5, use_raw=False, method='wilcoxon', prefix='disease', out_dir=None, save=True)

Compute disease-progression gene programs.

For each cell type present in both healthy and disease tissue, performs DE (disease cells of that type vs healthy cells of that type). Contamination genes (outlier low-score genes from global disease DE) are zeroed out before normalisation, following the sc-linker approach.

Parameters:

• adata (AnnData) – AnnData with both healthy and disease cells.

• celltype_col (str) – Column in adata.obs with cell-type labels.

• diagnosis_col (str) – Column in adata.obs with diagnosis / disease status.

• healthy_label (str) – Value in diagnosis_col that denotes healthy samples.

• disease_label (str) – Value in diagnosis_col that denotes disease samples.

• min_cells_per_group (int (default: 5)) – Minimum cells in each (healthy/disease x cell type) group.

• use_raw (bool (default: False)) – Passed to sc.tl.rank_genes_groups.

• method (str (default: 'wilcoxon')) – DE method.

• prefix (str (default: 'disease')) – Prefix for output file names.

• out_dir (str | Path | None (default: None)) – Directory to write CSV output files.

• save (bool (default: True)) – Whether to write output CSV files.

Return type:

dict[str, DataFrame]

Returns:

dict Same structure as compute_celltype_programs().