decoupler.mt.udt

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decoupler.mt.udt#

decoupler.mt.udt = <decoupler._Method.Method object>#

Univariate Decision Tree (UDT) [BiMVSB+22].

This approach uses the molecular features from one observation as the population of samples and it fits a gradient boosted decision trees model with a single covariate, which is the feature weights of a set FF. It uses the implementation provided by xgboost [CG16].

The enrichment score ESES is then calculated as the coefficient of determination R2R^2.

This method does not perform statistical testing on ESES and therefore does not return pvaluep_{value}.

Parameters:

  • data –

    anndata.AnnData instance, pandas.DataFrame, or a tuple of (matrix, samples, features). All methods assume that input values follow a normal distribution unless otherwise specified. Therefore, when working with observational count data, some form of normalization is required (e.g., scanpy’s library-size normalization followed by log1p). Using raw integer counts is not recommended, as they follow a Poisson distribution.

    Feature scaling on normalized counts is also acceptable, but note that it changes the results by assuming equal importance across features, and outcomes will vary depending on which observations are included.

    No normalization or transformation is required when using contrast-level feature statistics such as log fold changes or Wald test statistics.

  • net – Dataframe in long format. Must include source and target columns, and optionally a weight column.

  • tmin (default: 5) – Minimum number of targets per source. Sources with fewer targets will be removed.

  • layer – Layer key name of an anndata.AnnData instance.

  • raw (default: False) – Whether to use the .raw attribute of anndata.AnnData.

  • empty (default: True) – Whether to remove empty observations (rows) or features (columns).

  • bsize (default: 250000) – For large datasets in sparse format, this parameter controls how many observations are processed at once. Increasing this value speeds up computation but uses more memory.

  • verbose (default: False) – Whether to display progress messages and additional execution details.

  • kwargs – All other keyword arguments are passed to xgboost.XGBRegressor.

Returns:

Enrichment scores ESES and, if applicable, adjusted pvaluep_{value} by Benjamini-Hochberg.

Example

import decoupler as dc

adata, net = dc.ds.toy()
dc.mt.udt(adata, net, tmin=3)
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