decoupler.run_udt
- decoupler.run_udt(mat, net, source='source', target='target', weight='weight', min_leaf=5, min_n=5, seed=42, verbose=False, use_raw=True)
Univariate Decision Tree (UDT).
UDT fits a single regression decision tree for each sample and regulator, where the observed molecular readouts in mat are the response variable and the regulator weights in net are the explanatory one. Target features with no associated weight are set to zero. The obtained feature importance from the fitted model is the activity (udt_estimate) of a given regulator.
- 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_leafint
The minimum number of samples required to be at a leaf node.
- 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
UDT scores. Stored in .obsm[‘udt_estimate’] if mat is AnnData.
- pvalsDataFrame
Obtained p-values. Stored in .obsm[‘udt_pvals’] if mat is AnnData.