decoupler.run_gsva
- decoupler.run_gsva(mat, net, source='source', target='target', kcdf=False, mx_diff=True, abs_rnk=False, min_n=5, seed=42, verbose=False, use_raw=True)
Gene Set Variation Analysis (GSVA).
Wrapper to run GSVA.
- Parameters:
- matlist, pd.DataFrame or AnnData
List of [features, matrix], dataframe (samples x features) or an AnnData instance.
- netpd.DataFrame
Network in long format.
- sourcestr
Column name in net with source nodes.
- targetstr
Column name in net with target nodes.
- kcdfbool
Wether to use a Gaussian kernel or not during the non-parametric estimation of the cumulative distribution function. By default no kernel is used (faster), to reproduce GSVA original behaviour in R set to True.
- mx_diffbool
Changes how the enrichment statistic (ES) is calculated. If True (default), ES is calculated as the difference between the maximum positive and negative random walk deviations. If False, ES is calculated as the maximum positive to 0.
- abs_rnkbool
Used when mx_diff = True. If False (default), the enrichment statistic (ES) is calculated taking the magnitude difference between the largest positive and negative random walk deviations. If True, feature sets with features enriched on either extreme (high or low) will be regarded as ‘highly’ activated.
- min_nint
Minimum of targets per source. If less, sources are removed.
- seedint
Random seed to use.
- verbosebool
Whether to show progress.
- use_rawbool
Use raw attribute of mat if present.
- Returns:
- Returns gsva activity estimates or stores them in mat.obsm[‘gsva_estimate’].