A comparison of gene set analysis methods in terms of sensitivity, prioritization and specificity
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Identification of functional sets of genes associated with conditions of interest from omics data was first reported in 1999, and since, a plethora of enrichment methods were published for systematic analysis of gene sets collections including Gene Ontology and biological pathways. Despite their widespread usage in reducing the complexity of omics experiment results, their performance is poorly understood. Leveraging the existence of disease specific gene sets in KEGG and Metacore® databases, we compared the performance of sixteen methods under relaxed assumptions while using 42 real datasets (over 1,400 samples). Most of the methods ranked high the gene sets designed for specific diseases whenever samples from affected individuals were compared against controls via microarrays. The top methods for gene set prioritization were different from the top ones in terms of sensitivity, and four of the sixteen methods had large false positives rates assessed by permuting the phenotype of the samples. The best overall methods among those that generated reasonably low false positive rates, when permuting phenotypes, were PLAGE, GLOBALTEST, and PADOG. The best method in the category that generated higher than expected false positives was MRGSE.
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These rank-based tests have been found to give an effective ranking of biologically significant pathways.
“These tests have been found to give an effective ranking of biologically significant pathways (54)”
From limma powers differential expression analyses for RNA-sequencing and microarray studies (2015) · cites this paper as [54]
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- Authors
- Tarca, A. L., Bhatti, G., & Romero, R.
- Affiliations
- National Institutes of Health (US)
- Corresponding author
- Tarca, Adi L.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-11-15
- Volume
- 8
- Issue
- 11
- Pages
- e79217
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3829842
- OpenAlex
- W2084533385
- Cited by
- 203
- References
- 34
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 21 · 2023: 13 · 2022: 16 · 2021: 11 · 2020: 28 · 2019: 19 · 2018: 12 · 2017: 17 · 2016: 20 · 2015: 19 · 2014: 5
- FWCI
- 6.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, public-domain)
- Europe PMC (PubMed Central) (publishedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- U.S. Department of Health and Human Services
- National Institutes of Health
- National Institute of Child Health and Human Development
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- gene set enrichment analysis, method comparison, sensitivity and specificity, false positive rate, KEGG, plage, microarray data, omics data interpretation
- MeSH terms
- Electronic Data Processing, Humans, Oligonucleotide Array Sequence Analysis, Databases, Genetic
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