omicverse.single.aucell

omicverse.single.aucell(exp_mtx, signatures, auc_threshold=0.05, noweights=False, normalize=False, seed=None, num_workers=4, index=None, columns=None, gene_overlap_threshold=0.8)[source]

Calculate gene signature enrichment scores using AUCell algorithm.

AUCell quantifies gene set enrichment by calculating the Area Under the Curve (AUC) of gene rankings for each cell, providing a robust measure of pathway activity.

Parameters:

  • exp_mtx (Expression matrix (n_cells x n_genes) - DataFrame or sparse matrix)

  • signatures (Gene signatures or regulons for enrichment analysis)

  • (float) (gene_overlap_threshold)

  • (bool) (normalize)

  • (bool)

  • (int) (num_workers)

  • (int)

  • index (Custom row index for output DataFrame (default: None))

  • columns (Custom column names for output DataFrame (default: None))

  • (float)

Return type:

DataFrame

Returns:

pd.DataFrame: AUC enrichment scores with cells as rows and signatures as columns