decoupler.run_consensus
- decoupler.run_consensus(mat, net, source='source', target='target', weight='weight', min_n=5, verbose=False, use_raw=True, args={})
Consensus score from top methods.
This consensus score is calculated from the three top performer methods: ulm, mlm and wsum_norm. For each of these methods, the obtained activities are transformed into z-scores, first for positive values and then for negative ones. These two sets of z-score transformed activities are computed by subsetting the values bigger or lower than 0, then by mirroring the selected values into their opposite sign and finally calculating a classic z-score. This transformation ensures that values across methods are comparable, and that they remain in their original sign (active or inactive). The final consensus score is the mean across different methods. A p-value is then estimated from these using a cumulative distribution function.
- 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.
- min_nint
Minimum of targets per source. If less, sources are removed.
- verbosebool
Whether to show progress.
- use_rawbool
Use raw attribute of mat if present.
- argsdict
Parameters for the
decoupler.decouple
function.
- Returns:
- estimateDataFrame
Consensus scores. Stored in .obsm[‘consensus_estimate’] if mat is AnnData.
- pvalsDataFrame
Obtained p-values. Stored in .obsm[‘consensus_pvals’] if mat is AnnData.