The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Gene set enrichment analysis: performance evaluation and usage guidelines
Open accessAbstract
A central goal of biology is understanding and describing the molecular basis of plasticity: the sets of genes that are combinatorially selected by exogenous and endogenous environmental changes, and the relations among the genes. The most viable current approach to this problem consists of determining whether sets of genes are connected by some common theme, e.g. genes from the same pathway are overrepresented among those whose differential expression in response to a perturbation is most pronounced. There are many approaches to this problem, and the results they produce show a fair amount of dispersion, but they all fall within a common framework consisting of a few basic components. We critically review these components, suggest best practices for carrying out each step, and propose a voting method for meeting the challenge of assessing different methods on a large number of experimental data sets in the absence of a gold standard.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
A novel signaling pathway impact analysis
Tarca, A. L., Draghici, S., Khatri, P., Hassan, S. S., Mittal, P., Kim, J.-S., Kim, C. J., Kusanovic, J. P., & Romero, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btn577 · Cited by 1,031
Transcriptional regulation and function during the human cell cycle
Cho, R. J., Huang, M., Campbell, M. J., Dong, H., Steinmetz, L., Sapinoso, L., Hampton, G., Elledge, S. J., Davis, R. W., & Lockhart, D. J. (2001) · Nature Genetics · doi:10.1038/83751 · Cited by 455
Gene-set approach for expression pattern analysis
Nam, D., & Kim, S.-Y. (2008) · Briefings in Bioinformatics · doi:10.1093/bib/bbn001 · Cited by 338
Harbig, J. (2005) · Nucleic Acids Research · doi:10.1093/nar/gni027 · Cited by 141
Cited by
GSVA: gene set variation analysis for microarray and RNA-Seq data
Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update
Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S. L., Jagodnik, K. M., Lachmann, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw377 · Cited by 10,799
Väremo, L., Nielsen, J., & Nookaew, I. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt111 · Cited by 697
A network-biology perspective of microRNA function and dysfunction in cancer
Bracken, C. P., Scott, H. S., & Goodall, G. J. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.134 · Cited by 668
Holland, C. H., Tanevski, J., Perales-Patón, J., Gleixner, J., Kumar, M. P., Mereu, E., Joughin, B. A., Stegle, O., Lauffenburger, D. A., Heyn, H., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1949-z · Cited by 323
Metadata
- Authors
- Hung, J.-H., Yang, T.-H., Hu, Z., Weng, Z., & DeLisi, C.
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-09-07
- Volume
- 13
- Issue
- 3
- Pages
- 281-291
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- OpenAlex
- W2027895157
- Cited by
- 266
- References
- 7
- FWCI
- 5.29
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Gene set enrichment analysis: performance evaluation and usage guidelines. https://app.cochat.ai/papers/10.1093/bib/bbr049
- The paper
- Hung, J.-H., Yang, T.-H., Hu, Z., Weng, Z., & DeLisi, C. (2012). Gene set enrichment analysis: performance evaluation and usage guidelines. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbr049
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
