GSEA-P: a desktop application for Gene Set Enrichment Analysis

A Subramanian, H Kuehn, J Gould, P Tamayo… - …, 2007 - academic.oup.com
A Subramanian, H Kuehn, J Gould, P Tamayo, JP Mesirov
Bioinformatics, 2007academic.oup.com
Abstract Gene Set Enrichment Analysis (GSEA) is a computational method that assesses
whether an a priori defined set of genes shows statistically significant, concordant
differences between two biological states. We report the availability of a new version of the
Java based software (GSEA-P 2.0) that represents a major improvement on the previous
release through the addition of a leading edge analysis component, seamless integration
with the Molecular Signature Database (MSigDB) and an embedded browser that allows …
Abstract
Gene Set Enrichment Analysis (GSEA) is a computational method that assesses whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states. We report the availability of a new version of the Java based software (GSEA-P 2.0) that represents a major improvement on the previous release through the addition of a leading edge analysis component, seamless integration with the Molecular Signature Database (MSigDB) and an embedded browser that allows users to search for gene sets and map them to a variety of microarray platform formats. This functionality makes it possible for users to directly import gene sets from MSigDB for analysis with GSEA. We have also improved the visualizations in GSEA-P 2.0 and added links to a new form of concise gene set annotations called Gene Set Cards. These additions, as well as other improvements suggested by over 3500 users who have downloaded the software over the past year have been incorporated into this new release of the GSEA-P Java desktop program.
Availability:  GSEA-P 2.0 is freely available for academic and commercial users and can be downloaded from http://www.broad.mit.edu/GSEA
Contact:  mesirov@broad.mit.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Oxford University Press