decoupler.run_wmean
- decoupler.run_wmean(mat, net, source='source', target='target', weight='weight', times=1000, batch_size=10000, min_n=5, seed=42, verbose=False, use_raw=True)
Weighted mean (WMEAN).
WMEAN infers regulator activities by performing the weighted sum of the targets and weights, divided by the absolute sum of the weights to obtain an enrichment score (wmean_estimate). Furthermore, permutations of random target features can be performed to obtain a null distribution that can be used to compute a z-score (wmean_norm), or a corrected estimate (wmean_corr) by multiplying wmean_estimate by the minus log10 of the obtained empirical p-value.
- Parameters:
- matlist, DataFrame or AnnData
List of [features, matrix], dataframe (samples x features) or an AnnData instance.
- netDataFrame
Network in long format.
- sourcestr
Column name in net with source nodes.
- targetstr
Column name in net with target nodes.
- weightstr
Column name in net with weights.
- timesint
How many random permutations to do.
- batch_sizeint
Size of the batches to use. Increasing this will consume more memmory but it will run faster.
- 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:
- estimateDataFrame
WMEAN scores. Stored in .obsm[‘wmean_estimate’] if mat is AnnData.
- normDataFrame
Normalized WMEAN scores. Stored in .obsm[‘wmean_norm’] if mat is AnnData.
- corrDataFrame
Corrected WMEAN scores. Stored in .obsm[‘wmean_corr’] if mat is AnnData.
- pvalsDataFrame
Obtained p-values. Stored in .obsm[‘wmean_pvals’] if mat is AnnData.