cellink.tl.external.scores_to_covar#

cellink.tl.external.scores_to_covar(scores, out_file, gene_map=None, negate=False)#

Convert a genes × cell-types score DataFrame to a MAGMA gene covariate file.

All genes with at least one non-NaN score are included (no top/bottom threshold). This is the Python-API equivalent of make_magma_covar.py in the combined_pipeline.

The covariate file is tab-delimited with GENE as the index name, one column per cell type, and NA for missing values:

GENE          ExcL2-3   ExcL4   InhSST
ENSG00001234  0.52      0.01    0.08
ENSG00005678  0.11      0.43    0.22

Parameters:

scores (DataFrame) – DataFrame with genes as rows and cell types as columns. Index should contain gene identifiers (ENSG IDs or gene symbols).
out_file (str | Path) – Output .covar file path.
gene_map (str | Path | Series | None (default: None)) – Optional mapping from gene symbols to ENSG IDs — same formats as in scores_to_gmt(). Non-ENSG rows are dropped after mapping.
negate (bool (default: False)) – If True, multiply all scores by -1 before writing. Use this to test enrichment in genes with low scores (e.g. negative Vg).

Return type:

Path

Returns:

Path Path to the written covariate file.

Examples

>>> from cellink.tl.external import scores_to_covar, run_magma_gpa
>>> covar = scores_to_covar(specificity_df, "brainscope_seismic.covar")
>>> run_magma_gpa(gene_results="scz.genes.raw", gene_covar=str(covar), out_prefix="results/scz_gpa")

cellink.tl.external.scores_to_covar

Contents

cellink.tl.external.scores_to_covar#