title: gCMAP: user-friendly connectivity mapping with R.
abstract: Connections between disease phenotypes and drug effects can be made by identifying commonalities in the associated patterns of differential gene expression. Searchable databases that record the impacts of chemical or genetic perturbations on the transcriptome--here referred to as 'connectivity maps'--permit discovery of such commonalities. We describe two R packages, gCMAP and gCMAPWeb, which provide a complete framework to construct and query connectivity maps assembled from user-defined collections of differential gene expression data. Microarray or RNAseq data are processed in a standardized way, and results can be interrogated using various well-established gene set enrichment methods. The packages also feature an easy-to-deploy web application that facilitates reproducible research through automatic generation of graphical and tabular reports.
  
  
  METHODS
  The gCMAP and gCMAPWeb R packages are freely available for UNIX, Windows and Mac OS X operating systems at Bioconductor (http://www.bioconductor.org).
authors.affiliation: Department of Bioinformatics and Computational Biology, Genentech Inc., South San Francisco, CA 94080, USA.
authors.forename: Thomas
authors.initials: T
authors.lastname: Sandmann
authors.forename: Sarah K
authors.initials: SK
authors.lastname: Kummerfeld
authors.forename: Robert
authors.initials: R
authors.lastname: Gentleman
authors.forename: Richard
authors.initials: R
authors.lastname: Bourgon
mesh_terms: D000818:Animals; D002460:Cell Line; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D020407:Internet; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D014584:User-Computer Interface