Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Aravind N. Subramanian, Pablo Tamayo, Vamsi Krishna Mootha, Sayan Mukherjee, Benjamin L. Ebert, Michael A. Gillette, Amanda G. Paulovich, Scott L. Pomeroy, Todd R. Golub, Eric S. Lander, Jill P. Mesirov. Proceedings of the National Academy of Sciences, 2005-09-30
Topic: Brain proteins, microglia and lab models
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.
Why it matters
Describes Gene Set Enrichment Analysis (GSEA), a method and free software package for interpreting gene expression data by looking at groups of genes that share a biological function, chromosomal location or regulation. Its examples come from cancer data sets; it is not about Alzheimer's.
New to this subject? Start with our plain-English guide: Genetics →
How this study scored 59/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on strength of evidence, so that was left out: it earned 56 of the 95 points we could judge it on, which is 59 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Proceedings of the National Academy of Sciences) |
| Journal's citation record | 4 / 4 | Proceedings of the National Academy of Sciences: journal h-index 1,167 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | — / 5 | No evidence rating recorded — left out of this score |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2005 — older than 20 years, still counted |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 7 / 7 | Cited 53,751 times (OpenAlex, 2026-10-10) |
| Citations per year | 5 / 5 | 2,337.0 citations a year over 23 year(s) since 2005 |
| Impact for its field and year | 6 / 6 | Cited 97.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 4 / 4 | Cited 15,272 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Jill P. Mesirov: h-index 87 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Harvard University: institution h-index 3,235 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 16199517) |
| Free to read | 2 / 2 | Free to read |
| Points | 56 / 95 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 56 of the 95 points we could judge it on is 59 out of 100. |
| Total | 59 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 11 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Subramanian, A. N., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., et al. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0506580102
Vancouver
Subramanian AN, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences. 2005. doi:10.1073/pnas.0506580102. PMID: 16199517.
BibTeX
@article{subramanian2005-16199517,
author = {Subramanian, Aravind N. and Tamayo, Pablo and Mootha, Vamsi Krishna and Mukherjee, Sayan and Ebert, Benjamin L. and Gillette, Michael A. and Paulovich, Amanda G. and Pomeroy, Scott L. and Golub, Todd R. and Lander, Eric S. and Mesirov, Jill P.},
title = {{Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles}},
journal = {Proceedings of the National Academy of Sciences},
year = {2005},
doi = {10.1073/pnas.0506580102},
pmid = {16199517},
url = {https://doi.org/10.1073/pnas.0506580102}
}