pyviper.NaRnEA package

Submodules

pyviper.NaRnEA.NaRnEA_classic module

pyviper.NaRnEA.NaRnEA_meta module

Allows the individual to infer normalized enrichment scores and proportional enrichment scores from gene expression data using the Nonparametric Analytical Rank-based Enrichment Analysis (NaRnEA)[1] function.

NaRnEA is an updated basis for 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 recommend 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 regulator that has more targets than others will have “boosted” NES scores, such that they cannot be compared to those with fewer targets.

param gex_data:

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

param interactome:

An object of class Interactome.

param layer:

The layer in the anndata object to use as the gene expression input.

type layer:

default: None

param eset_filter:

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

type eset_filter:

default: False

param min_targets:

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.

type min_targets:

default: 30

param verbose:

Whether extended output about the progress of the algorithm should be given.

type verbose:

default: True

returns:

A dictionary containing :class:`~numpy.ndarray` containing NES values (key

rtype:

‘nes’) and PES values (key: ‘pes’).

References

[1] Griffin, A. T., Vlahos, L. J., Chiuzan, C., & Califano, A. (2023). NaRnEA: An Information Theoretic Framework for Gene Set Analysis. Entropy, 25(3), 542.

Module contents

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

Allows the individual to infer normalized enrichment scores and proportional enrichment scores from gene expression data using the Nonparametric Analytical Rank-based Enrichment Analysis (NaRnEA)[1] function.

NaRnEA is an updated basis for 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 recommend 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 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.

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

Returns:

A dictionary containing :class:`~numpy.ndarray` containing NES values (key

Return type:

‘nes’) and PES values (key: ‘pes’).

References

[1] Griffin, A. T., Vlahos, L. J., Chiuzan, C., & Califano, A. (2023). NaRnEA: An Information Theoretic Framework for Gene Set Analysis. Entropy, 25(3), 542.