Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles

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...

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Published inProceedings of the National Academy of Sciences - PNAS Vol. 102; no. 43; pp. 15545 - 15550
Main Authors Subramanian, Aravind, Tamayo, Pablo, Mootha, Vamsi K., Mukherjee, Sayan, Benjamin L. Ebert, Michael A. Gillette, Amanda Paulovich, Pomeroy, Scott L., Golub, Todd R., Lander, Eric S., Mesirov, Jill P.
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 25.10.2005
National Acad Sciences
SeriesFrom the Cover
Subjects
Biological Sciences
Biomedical research
Cell Line, Tumor
Cell lines
Correlations
Cytogenetics
Datasets
Female
Gene expression
Gene Expression Profiling - methods
Genes
Genes, p53 - physiology
Genome
Humans
Leukemia
Leukemia, Myeloid, Acute - genetics
Lung cancer
Lung neoplasms
Lung Neoplasms - genetics
Lung Neoplasms - mortality
Male
Myeloid leukemia
Oligonucleotide Array Sequence Analysis
P values
Phenotypes
Precursor Cell Lymphoblastic Leukemia-Lymphoma - genetics
Ribonucleic acid
RNA
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Summary: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.
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A.S. and P.T. contributed equally to this work.
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.
See Commentary on page 15278.
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.
Contributed by Eric S. Lander, August 2, 2005
To whom correspondence may be addressed. E-mail: lander@broad.mit.edu or mesirov@broad.mit.edu.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.0506580102