pyviper.aREA package

Submodules

pyviper.aREA.aREA_classic module

pyviper.aREA.aREA_meta module

Module contents

pyviper.aREA.aREA(gex_data, interactome, layer=None, eset_filter=False, min_targets=30, mvws=1, verbose=True)

Allows the individual to infer normalized enrichment scores from gene expression data using the Analytical Ranked Enrichment Analysis (aREA)[1] function.

It is the original basis of the VIPER (Virtual Inference of Protein-activity by Enriched Regulon analysis) algorithm.

The Interactome object must not contain any targets that are not in the features of gex_data. This can be accomplished by running:

interactome.filter_targets(gex_data.var_names)

It is highly recommended to do this on the unPruned network and then prune to ensure the pruned network contains a consistent number of targets per regulator, all of which exist within gex_data. A consistent number of targets allows regulators to have NES scores that are comparable to one another. A regulator that has more targets than others will have “boosted” NES scores, such that they cannot be compared to those with fewer targets.

Parameters:

  • gex_data – Gene expression stored in an anndata object (e.g. from Scanpy) or in a pd.DataFrame.

  • interactome – An object of class Interactome.

  • layer (default: None) – The layer in the anndata object to use as the gene expression input.

  • eset_filter (default: False) – Whether to filter out genes not present in the interactome (True) or to keep this biological context (False). This will affect gene rankings.

  • min_targets (default: 30) – The minimum number of targets that each regulator in the interactome should contain. Regulators that contain fewer targets than this minimum will be culled from the network (via the Interactome.cull method). The reason users may choose to use this threshold is because adequate targets are needed to accurately predict enrichment.

  • mvws (default: 1) – (A) Number indicating either the exponent score for the metaViper weights. These are only applicable when enrichment = ‘area’ and are not used when enrichment = ‘narnea’. Roughly, a lower number (e.g. 1) results in networks being treated as a consensus network (useful for multiple networks of the same celltype with the same epigenetics), while a higher number (e.g. 10) results in networks being treated as separate (useful for multiple networks of different celltypes with different epigenetics). (B) The name of a column in gex_data that contains the manual assignments of samples to networks using list position or network names. (C) “auto”: assign samples to networks based on how well each network allows for sample enrichment.

  • verbose (default: True) – Whether extended output about the progress of the algorithm should be given.

Return type:

A dataframe of DataFrame containing NES values.

References

[1] Alvarez, M. J., Shen, Y., Giorgi, F. M., Lachmann, A., Ding, B. B., Ye, B. H., & Califano, A. (2016). Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nature genetics, 48(8), 838-847.