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Published online on September 30, 2005, 10.1073/pnas.0506580102
PNAS | October 25, 2005 | vol. 102 | no. 43 | 15545-15550
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From the Cover
GENETICS
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Aravind Subramanian a b, Pablo Tamayo a b, Vamsi K. Mootha a, c, Sayan Mukherjee d, Benjamin L. Ebert a, e, Michael A. Gillette a, f, Amanda Paulovich g, Scott L. Pomeroy h, Todd R. Golub a, e, Eric S. Lander a, c, i, j, k, and Jill P. Mesirov a, k

aBroad Institute of Massachusetts Institute of Technology and Harvard, 320 Charles Street, Cambridge, MA 02141;cDepartment of Systems Biology, Alpert 536, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02446; dInstitute for Genome Sciences and Policy, Center for Interdisciplinary Engineering, Medicine, and Applied Sciences, Duke University, 101 Science Drive, Durham, NC 27708; eDepartment of Medical Oncology, Dana–Farber Cancer Institute, 44 Binney Street, Boston, MA 02115; fDivision of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114; gFred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, C2-023, P.O. Box 19024, Seattle, WA 98109-1024; hDepartment of Neurology, Enders 260, Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115; iDepartment of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142; and jWhitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142

Contributed by Eric S. Lander, August 2, 2005

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.

microarray

Author contributions: A.S., P.T., V.K.M., E.S.L., and J.P.M. designed research; A.S., P.T., V.K.M., E.S.L., and J.P.M. performed research; A.S., P.T., V.K.M., S.M., E.S.L., and J.P.M. contributed new reagents/analytic tools; A.S., P.T., V.K.M., B.L.E., M.A.G., T.R.G., E.S.L., and J.P.M. analyzed data; A.S., P.T., V.K.M., E.S.L., and J.P.M. wrote the paper; and A.P. and S.L.P. contributed data.

Freely available online through the PNAS open access option.

Abbreviations: ALL, acute lymphoid leukemia; AML, acute myeloid leukemia; ES, enrichment score; FDR, false discovery rate; GSEA, Gene Set Enrichment Analysis; MAPK, mitogen-activated protein kinase; MSigDB, Molecular Signature Database; NES, normalized enrichment score.

See Commentary on page 15278.

b A.S. and P.T. contributed equally to this work.

k To whom correspondence may be addressed. E-mail: lander{at}broad.mit.edu or mesirov{at}broad.mit.edu.

© 2005 by The National Academy of Sciences of the USA


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Related Commentary in PNAS:

Application of a priori established gene sets to discover biologically important differential expression in microarray data
Andrea Bild and Phillip George Febbo
PNAS 2005 102: 15278-15279. [Extract] [Full Text]  



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