Cited by 224Bioinformatics and Genomic NetworksBiomedical Text Mining and OntologiesGene expression and cancer classification

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A Comparison of Gene Set Analysis Methods in Terms of Sensitivity, Prioritization and Specificity

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Tarca, Adi L., Bhatti, Gaurav, Romero, Roberto·2013·PLoS ONE

DOI 10.1371/journal.pone.0079217PMC PMC3829842

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Abstract

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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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
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
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
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
PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes
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
Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray
Schena, M., Shalon, D., Davis, R. W., & Brown, P. O. (1995) · Science · doi:10.1126/science.270.5235.467 · Cited by 9,609
limma: Linear Models for Microarray Data
Smyth, G. K. (2005) · Statistics for Biology and Health · doi:10.1007/0-387-29362-0_23 · Cited by 5,796
Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1
Barbie, D. A., Tamayo, P., Boehm, J. S., Kim, S. Y., Moody, S. E., Dunn, I. F., Schinzel, A. C., Sandy, P., Meylan, E., Scholl, C., et al. (2009) · Nature · doi:10.1038/nature08460 · Cited by 3,922
Bioinformatics and Computational Biology Solutions Using R and Bioconductor
Gentleman, R. C., Carey, V. J., Huber, W. K., Irizarry, R. A., & Dudoit, S. (2005) · Statistics in the health sciences · doi:10.1007/0-387-29362-0 · Cited by 3,470

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S Draghici, 2003, Global functional profiling of gene expression, Genomics, 81, 98
VK Mootha, 2003, PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes, Nat Genet, 34, 267, 10.1038/ng1180 · DOI · Paper page

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Authors: Tarca, A. L., Bhatti, G., & Romero, R.
Affiliations: National Institutes of Health (US)
Corresponding author: Tarca, Adi L.
ORCID: Tarca, Adi L.
Bhatti, Gaurav
Romero, Roberto
Venue: PLoS ONE
Publisher: Public Library of Science (PLoS)
Published: 2013-11-15
Volume: 8
Issue: 11
Pages: e79217
ISSN: 1932-6203 (ISSN-L)
DOI: 10.1371/journal.pone.0079217
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: public-domain
creativecommons.org/publicdomain/zero/1.0/ (unspecified)
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
Health Sciences › Medicine › Health Informatics › 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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A Comparison of Gene Set Analysis Methods in Terms of Sensitivity, Prioritization and Specificity

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