Pathway Expression Profiles (PEPs) are based on the expression of pathways (defined as sets of genes) as opposed to individual genes. This package converts gene expression profiles to PEPs and performs enrichment analysis of both pathways and experimental conditions, such as "Drug Set Enrichment Analysis" (finding pathways that are consistently dysregulated by a set of drugs) and "gene2drug" analysis (finding drugs that dysregulate a set of pathways or a single gene).
Two papers have been published in Bioinformatics covering gep2pep methods:
Two corresponding webtools are online, which use Cmap data for both types of analysis:
The latest stable release can be downloaded from Bioconductor at https://bioconductor.org/packages/release/bioc/html/gep2pep.html. The latest development versions is at https://bioconductor.org/packages/devel/bioc/html/gep2pep.html. Installation instructions ar provided there.
Additional in progress versions can be found on Github at https://github.com/franapoli/gep2pep, downloaded and then installed as follows:
> install.packages("path-to-downloaded-source", repos=NULL)
Bug fixes, submitted to Bioconductor.
Added "SGE mode", including the function
is to support fast computation of Kolmogorov-Smirnov statistics for
large collections of gene sets including a single gene, which is
useful to support gene-centric (as opposed to pathway-centric)
gene2pathways now accepts a list of genes and returns all the
pathways including either ALL of them or ANY of them according to
added support to deal with MsigDB release 6.1, which contains unconventional set categories ("ARCHIVED")
added raw-mode to deal with large datasets. Raw mode stores PEPs to separate files during conversion, thus can be easily parallelized
added "organism" parameter to
importMSigDB to select sets
added hdf5 support for large collections of PEPs
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