The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Gene set enrichment analysis made simple
Open accessAbstract
Among the many applications of microarray technology, one of the most popular is the identification of genes that are differentially expressed in two conditions. A common statistical approach is to quantify the interest of each gene with a p-value, adjust these p-values for multiple comparisons, choose an appropriate cut-off, and create a list of candidate genes. This approach has been criticised for ignoring biological knowledge regarding how genes work together. Recently a series of methods, that do incorporate biological knowledge, have been proposed. However, the most popular method, gene set enrichment analysis (GSEA), seems overly complicated. Furthermore, GSEA is based on a statistical test known for its lack of sensitivity. In this article we compare the performance of a simple alternative to GSEA. We find that this simple solution clearly outperforms GSEA. We demonstrate this with eight different microarray datasets.
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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
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
Mootha, V. K., Lindgren, C. M., Eriksson, K.-F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstråle, M., Laurila, E., et al. (2003) · Nature Genetics · doi:10.1038/ng1180 · Cited by 10,197
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
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Wu, T. D., Madireddi, S., de Almeida, P. E., Banchereau, R., Chen, Y.-J. J., Chitre, A. S., Chiang, E. Y., Iftikhar, H., O’Gorman, W. E., Au-Yeung, A., et al. (2020) · Nature · doi:10.1038/s41586-020-2056-8 · Cited by 770
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Haffner, M. C., Aryee, M. J., Toubaji, A., Esopi, D. M., Albadine, R., Gurel, B., Isaacs, W. B., Bova, G. S., Liu, W., Xu, J., et al. (2010) · Nature Genetics · doi:10.1038/ng.613 · Cited by 608
A dual function of the CRISPR–Cas system in bacterial antivirus immunity and DNA repair
Babu, M., Beloglazova, N., Flick, R., Graham, C., Skarina, T., Nocek, B., Gagarinova, A., Pogoutse, O., Brown, G., Binkowski, A., et al. (2011) · Molecular Microbiology · doi:10.1111/j.1365-2958.2010.07465.x · Cited by 250
Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities
Shah, I., Bundy, J., Chambers, B., Everett, L. J., Haggard, D., Harrill, J., Judson, R. S., Nyffeler, J., & Patlewicz, G. (2022) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.2c00245 · Cited by 19
Metadata
- Authors
- Irizarry, R. A., Wang, C., Zhou, Y., & Speed, T. P.
- Venue
- Statistical Methods in Medical Research
- Publisher
- SAGE Publications
- Published
- 2009-12
- Volume
- 18
- Issue
- 6
- Pages
- 565-575
- ISSN
- 0962-2802 (ISSN-L)
- 1477-0334
- OpenAlex
- W2078284062
- Cited by
- 194
- References
- 4
- FWCI
- 4.20
- Type
- journal article
- Language
- en
- Open access
- green
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