%0 Journal Article %1 Subramanian:2005:Proc-Natl-Acad-Sci-U-S-A:16199517 %A Subramanian, A %A Tamayo, P %A Mootha, V K %A Mukherjee, S %A Ebert, B L %A Gillette, M A %A Paulovich, A %A Pomeroy, S L %A Golub, T R %A Lander, E S %A Mesirov, J P %D 2005 %J Proc Natl Acad Sci U S A %K Analysis Enrichment GSEA %N 43 %P 15545-15550 %R 10.1073/pnas.0506580102 %T Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles %U http://www.ncbi.nlm.nih.gov/pubmed/16199517 %V 102 %X 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{Subramanian:2005:Proc-Natl-Acad-Sci-U-S-A:16199517, 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.}, added-at = {2012-09-15T11:29:19.000+0200}, author = {Subramanian, A and Tamayo, P and Mootha, V K and Mukherjee, S and Ebert, B L and Gillette, M A and Paulovich, A and Pomeroy, S L and Golub, T R and Lander, E S and Mesirov, J P}, biburl = {https://www.bibsonomy.org/bibtex/2a7e30e292232f2a7a8667e7dede33488/sbsad}, description = {Gene set enrichment analysis: a kno... [Proc Natl Acad Sci U S A. 2005] - PubMed - NCBI}, doi = {10.1073/pnas.0506580102}, interhash = {6c83a69786c883de3dccc7979d6d2484}, intrahash = {a7e30e292232f2a7a8667e7dede33488}, journal = {Proc Natl Acad Sci U S A}, keywords = {Analysis Enrichment GSEA}, month = oct, number = 43, pages = {15545-15550}, pmid = {16199517}, timestamp = {2012-09-15T11:35:05.000+0200}, title = {Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16199517}, volume = 102, year = 2005 }