Skip to content

franapoli/gep2pep

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

78 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

output
github_document

gep2pep

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:

  • A paper about Drug Set Enrichment Analysis.
  • A paper about gene2drug analysis.

Two corresponding webtools are online, which use Cmap data for both types of analysis:

Gep2pep is maintained by Francesco Napolitano alt text

Download and Installation

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)

News

v1.3.1

  • Bug fixes, submitted to Bioconductor.

  • Added "SGE mode", including the function addSingleGeneSets. This 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) analysis.

  • gene2pathways now accepts a list of genes and returns all the pathways including either ALL of them or ANY of them according to the new and paramater.

v1.1.1

  • 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

v1.0.0

  • First release

About

Gene Expression Profiles to Pathway Expression Profiles conversion and analysis

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages