Finds "Local Subnetworks" Within an Interaction Network which Show Enrichment for Differentially Expressed Genes

Implements the method described in "Network-based analysis of omics data: The LEAN method" [Gwinner Boulday (2016) ] Given a protein interaction network and a list of p-values describing a measure of interest (as e.g. differential gene expression) this method computes an enrichment p-value for the protein neighborhood of each gene and compares it to a background distribution of randomly drawn p-values. The resulting scores are corrected for multiple testing and significant hits are returned in tabular format.


Implements the method described in [Gwinner et al., Network-based analysis of omics data: The LEAN method, Bioinformatics 2016]. Given a protein interaction network and a list of p-values describing a measure of interest (as e.g. differential gene expression) this method computes an enrichment p-value for the protein neighborhood of each gene and compares it to a background distribution of randomly drawn p-values. The resulting scores are corrected for multiple testing and significant hits are returned in tabular format.

See help page of run.lean for a more detailed description of how to use this package (type "?run.lean" in R prompt to do so)
Type vignette("CCM-data") for an example showing the application of LEAN to the CCM knockout data set discussed in the paper.
Type vignette("subnet-sim") for an example showing the application of LEAN to simulated subnetwork data discussed in the paper.

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install.packages("LEANR")

1.4.9 by Frederik Gwinner, 3 months ago


Browse source code at https://github.com/cran/LEANR


Authors: Frederik Gwinner


Documentation:   PDF Manual  


GPL-3 license


Depends on igraph, foreach

Suggests knitr, doMC, rmarkdown, ROCR, testthat


See at CRAN