Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

by: Aravind Subramanian, Pablo Tamayo, Vamsi K Mootha, Sayan Mukherjee, Benjamin L Ebert, Michael A Gillette, Amanda Paulovich, Scott L Pomeroy, Todd R Golub, Eric S Lander, Jill P Mesirov

Proceedings of the National Academy of Sciences of the USA, Vol. 102, No. 43. (25 October 2005), pp. 15545-15550.

View FullText article

DOI, HighWire, HighWire, Pubmed, Hubmed

Reviews [Write a review of this article]

There are no reviews of this article

Find related articles from these CiteULike users

ihpark, chrisamiller, dragonrez, jfr, balajis, maryee, xwbcn, hpiwowar, aviad_work, ealloza, jdiggans, jeremymiller, frohike, hawkeyehammer, schuylrp, natstreet, Bioinformatics (group), BioinfoCIPF (group), GeschwindLab (group), Indigene (group), heliopais, shikin, waghsk, roys, ellakat, srp33, sgoetz, AbnerCYH

Find related articles with these CiteULike tags

algorithm, all, analysis, bioinformatics, biology, broad, chip, complex, comps, copa, data_mining, enrichment, enrichment_analysis, expression, functional_annotation, gene, gene_expression, gene_set_analysis, gene_sets, genomics, go, gsea, human, jim, kdd, method, microarray, microarray_analysis, networks, no-tag, nsf, omr, pathway, pathway_analysis, pathway-analysis, pathway_perturbation, probmod, regulon-paper, set, signature, statistics

Abstract

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

@article{subramanian2005, abstract = {Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.}, author = {Subramanian, Aravind and Tamayo, Pablo and Mootha, Vamsi K. and Mukherjee, Sayan and Ebert, Benjamin L. and Gillette, Michael A. and Paulovich, Amanda and Pomeroy, Scott L. and Golub, Todd R. and Lander, Eric S. and Mesirov, Jill P. }, citeulike-article-id = {525366}, doi = {http://dx.doi.org/10.1073/pnas.0506580102}, journal = {Proceedings of the National Academy of Sciences of the USA}, keywords = {gene\_sets}, month = {October}, number = {43}, pages = {15545--15550}, posted-at = {2008-02-27 09:52:21}, priority = {0}, title = {Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles}, url = {http://dx.doi.org/10.1073/pnas.0506580102}, volume = {102}, year = {2005} }

RIS record

TY - JOUR ID - subramanian2005 L3 - citeulike-article-id:525366 TI - Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles JF - Proceedings of the National Academy of Sciences of the USA VL - 102 IS - 43 SP - 15545 EP - 15550 N2 - Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets. KW - gene_sets AU - Subramanian, Aravind AU - Tamayo, Pablo AU - Mootha, Vamsi AU - Mukherjee, Sayan AU - Ebert, Benjamin AU - Gillette, Michael AU - Paulovich, Amanda AU - Pomeroy, Scott AU - Golub, Todd AU - Lander, Eric AU - Mesirov, Jill PY - 2005/10/25/ UR - http://dx.doi.org/10.1073/pnas.0506580102 ER -

RIS BibTeX