--- output: github_document --- <!-- Grab your social icons from https://github.com/carlsednaoui/gitsocial --> [1.2]: http://i.imgur.com/wWzX9uB.png (me on Twitter) [1]: http://www.twitter.com/franapoli <!-- Grab your social icons from https://github.com/carlsednaoui/gitsocial --> ## 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](http://rdcu.be/pklt) about Drug Set Enrichment Analysis. - A [paper](https://academic.oup.com/bioinformatics/article/34/9/1498/4721786) about gene2drug analysis. Two corresponding webtools are online, which use Cmap data for both types of analysis: - [dsea.tigem.it](http://dsea.tigem.it) - [gene2drug.tigem.it](http://gene2drug.tigem.it) Gep2pep is maintained by Francesco Napolitano [![alt text][1.2]][1] ## Download and Installation The latest stable release can be downloaded from Bioconductor at [https://bioconductor.org/packages/release/bioc/html/gep2pep.html](https://bioconductor.org/packages/release/bioc/html/gep2pep.html). The latest development versions is at [https://bioconductor.org/packages/devel/bioc/html/gep2pep.html](https://bioconductor.org/packages/devel/bioc/html/gep2pep.htmlFurther). Installation instructions ar provided there. Additional in progress versions can be found on Github at [https://github.com/franapoli/gep2pep](https://www.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