Gene-set approach for expression pattern analysis
Open accessAbstract
Recently developed gene set analysis methods evaluate differential expression patterns of gene groups instead of those of individual genes. This approach especially targets gene groups whose constituents show subtle but coordinated expression changes, which might not be detected by the usual individual gene analysis. The approach has been quite successful in deriving new information from expression data, and a number of methods and tools have been developed intensively in recent years. We review those methods and currently available tools, classify them according to the statistical methods employed, and discuss their pros and cons. We also discuss several interesting extensions to the methods.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Genetic pathway analysis methods for GWAS data aim to identify which biological pathways show an excess of etiological association.
“Genetic pathway analysis methods for GWAS data have been developed5,6 and aim to identify which biological pathways show an excess of etiological association”
From Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways (2015) · cites this paper as [5]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
1 retracted
Genomic signatures to guide the use of chemotherapeutics
Potti, A., Dressman, H. K., Bild, A., Riedel, R. F., Chan, G., Sayer, R. A., Cragun, J. M., Cottrill, H. M., Kelley, M. J., Petersen, R. P., et al. (2006) · Nature Medicine · doi:10.1038/nm1491 · Cited by 595 · see citing works
RetractedSubramanian, 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 · see citing works
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 · see citing works
Oncogenic pathway signatures in human cancers as a guide to targeted therapies
Bild, A., Yao, G., Chang, J. T., Wang, Q., Potti, A., Chasse, D. A. D., Joshi, M.-B., Harpole, D. H., Lancaster, J. M., Berchuck, A., et al. (2005) · Nature · doi:10.1038/nature04296 · Cited by 2,032 · see citing works
Microarray data analysis: from disarray to consolidation and consensus
Allison, D. B., Cui, X., Page, G. P., & Sabripour, M. (2006) · Nature Reviews Genetics · doi:10.1038/nrg1749 · Cited by 1,339 · see citing works
A global test for groups of genes: testing association with a clinical outcome
Goeman, J. J., van de Geer, S., de Kort, F., & van Houwelingen, H. C. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg382 · Cited by 1,052 · see citing works
Ontological analysis of gene expression data: current tools, limitations, and open problems
Khatri, P., & Drăghici, S. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti565 · Cited by 850 · see citing works
Analyzing gene expression data in terms of gene sets: methodological issues
Goeman, J. J., & Bühlmann, P. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm051 · Cited by 795 · see citing works
On testing the significance of sets of genes
Efron, B., & Tibshirani, R. J. (2007) · The Annals of Applied Statistics · doi:10.1214/07-aoas101 · Cited by 785 · see citing works
PAGE: Parametric Analysis of Gene Set Enrichment
Kim, S., & Volsky, D. J. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-144 · Cited by 781 · see citing works
Self-citationEnrichment or depletion of a GO category within a class of genes: which test?
Rivals, I., Personnaz, L., Taing, L., & Potier, M. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl633 · Cited by 722 · see citing works
Discovering statistically significant pathways in expression profiling studies
Tian, L., Greenberg, S. A., Kong, S. W., Altschuler, J., Kohane, I. S., & Park, P. J. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506577102 · Cited by 649 · see citing works
Pathway level analysis of gene expression using singular value decomposition
Tomfohr, J. K., Lü, J., & Kepler, T. B. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-225 · Cited by 421 · see citing works
Barry, W. T., Nobel, A. B., & Wright, F. A. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti260 · Cited by 349 · see citing works
GeneTrail--advanced gene set enrichment analysis
Backes, C., Keller, A., Küntzer, J., Kneissl, B., Comtesse, N., Elnakady, Y. A., Müller, R., Meese, E. U., & Lenhof, H. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm323 · Cited by 341 · see citing works
Virtaneva, K. I., Wright, F. A., Tanner, S. M., Yuan, B., Lemon, W. J., Caligiuri, M. A., Bloomfield, C. D., de la Chapelle, A., & Krahe, R. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.3.1124 · Cited by 311 · see citing works
ErmineJ: Tool for functional analysis of gene expression data sets
Lee, H. K., Braynen, W., Keshav, K. D., & Pavlidis, P. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-269 · Cited by 278 · see citing works
BABELOMICS: a systems biology perspective in the functional annotation of genome-scale experiments
Al‐Shahrour, F., Mínguez, P., Tárraga, J., Montaner, D. A., Alloza, E., Vaquerizas, J. M., Conde, L., Blaschke, C., Vera, J., & Dopazo, J. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl172 · Cited by 269 · see citing works
Improving gene set analysis of microarray data by SAM-GS
Dinu, I. A., Potter, J. D., Mueller, T., Liu, Q., Adewale, A. J., Jhangri, G. S., Einecke, G., Famulski, K. S., Halloran, P. F., & Yasui, Y. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-242 · Cited by 262 · see citing works
Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis
Newton, M. A., Quintana, O. A., den Boon, J. A., Sengupta, S., & Ahlquist, P. (2016) · Cited by 230 · see citing works
Extensions to gene set enrichment
Jiang, Z., & Gentleman, R. C. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl599 · Cited by 216 · see citing works
T-profiler: scoring the activity of predefined groups of genes using gene expression data
Boorsma, A., Foat, B. C., Vis, D. J., Klis, F. M., & Bussemaker, H. J. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki484 · Cited by 206 · see citing works
Testing association of a pathway with survival using gene expression data
Goeman, J. J., Oosting, J., Cleton-Jansen, A., et al. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti267 · Cited by 170 · see citing works
Group testing for pathway analysis improves comparability of different microarray datasets
Manoli, T., Gretz, N., Gröne, H., Kenzelmann, M., Eils, R., & Brors, B. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl424 · Cited by 150 · see citing works
A multivariate approach for integrating genome-wide expression data and biological knowledge
Kong, S. W., Pu, W. T., & Park, P. J. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl401 · Cited by 132 · see citing works
From genes to functional classes in the study of biological systems
Al‐Shahrour, F., Arbiza, L., Dopazo, H., Huerta‐Cepas, J., Mínguez, P., Montaner, D. A., & Dopazo, J. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-114 · Cited by 122 · see citing works
Statistical concerns about the GSEA procedure
Damian, D., & Gorfine, M. (2004) · Nature Genetics · doi:10.1038/ng0704-663a · Cited by 121 · see citing works
EXPLORING GENE EXPRESSION DATA WITH CLASS SCORES
Pavlidis, P., Lewis, D. P., & Noble, W. S. (2001) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789812799623_0044 · Cited by 118 · see citing works
Pan, K., Lih, C., & Cohen, S. N. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0502674102 · Cited by 102 · see citing works
Functional Interpretation of Microarray Experiments
Dopazo, J. (2006) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2006.10.398 · Cited by 90 · see citing works
Comparing functional annotation analyses with Catmap
Breslin, T., Edén, P., & Krogh, M. (2004) · BMC Bioinformatics · doi:10.1186/1471-2105-5-193 · Cited by 87 · see citing works
Edelman, E. J., Porrello, A., Guinney, J., Balakumaran, B. S., Bild, A., Febbo, P. G., & Mukherjee, S. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl231 · Cited by 73 · see citing works
Ben‐Shaul, Y., Bergman, H., & Soreq, H. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bti149 · Cited by 60 · see citing works
Absolute enrichment: gene set enrichment analysis for homeostatic systems
Saxena, V., Orgill, D. P., & Kohane, I. S. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl766 · Cited by 58 · see citing works
Smid, M., & Dorssers, L. C. J. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth293 · Cited by 52 · see citing works
Kim, S.-B., Kim, S.-B., Yang, S., et al. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm144 · Cited by 48 · see citing works
Self-citationDelongchamp, R. R., Lee, T., & Velasco, C. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-s2-s11 · Cited by 46 · see citing works
GOAL: automated Gene Ontology analysis of expression profiles
Volinia, S., Evangelisti, R., Francioso, F., et al. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh443 · Cited by 42 · see citing works
Eu.Gene Analyzer a tool for integrating gene expression data with pathway databases
Cavalieri, D., Castagnini, C., Toti, S., et al. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm333 · Cited by 41 · see citing works
ADGO: analysis of differentially expressed gene sets using composite GO annotation
Nam, D., Kim, S.-B., Kim, S., Yang, S., Kim, S., & Chu, I. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl378 · Cited by 39 · see citing works
Self-citationScheer, M. P., Klawonn, F., Münch, R., et al. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl329 · Cited by 32 · see citing works
A rapid method for microarray cross platform comparisons using gene expression signatures
Cheadle, C., Becker, K. G., Cho‐Chung, Y. S., Nesterova, M., Watkins, T., Wood, W. H., Prabhu, V. V., & Barnes, K. C. (2006) · Molecular and Cellular Probes · doi:10.1016/j.mcp.2006.07.004 · Cited by 28 · see citing works
Domain-enhanced analysis of microarray data using GO annotations
Liu, J., Hughes‐Oliver, J. M., & Menius, J. A. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm092 · Cited by 28 · see citing works
The high-level similarity of some disparate gene expression measures
Raghavan, N., Bondt, A. M. I. M. D., Talloen, W., et al. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm448 · Cited by 17 · see citing works
MEGO: Gene Functional Module Expression Based on Gene Ontology
Tu, K., Yu, H., & Zhu, M. (2005) · BioTechniques · doi:10.2144/05382rr04 · Cited by 16 · see citing works
Reply to "Statistical concerns about the GSEA procedure"
Mootha, V. K., Daly, M. J., Patterson, N., et al. (2004) · Nature Genetics · doi:10.1038/ng0704-663b · Cited by 14 · see citing works
Discovering tightly regulated and differentially expressed gene sets in whole genome expression data
Ye, C., & Eskin, E. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btl315 · Cited by 12 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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 · see citing works
Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation
Merico, D., Isserlin, R., Stueker, O., Emili, A., & Bader, G. D. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013984 · Cited by 2,235 · see citing works
GAGE: generally applicable gene set enrichment for pathway analysis
Luo, W., Friedman, M. S., Shedden, K., Hankenson, K. D., & Woolf, P. J. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-161 · Cited by 1,333 · see citing works
Bock, C., Kiskinis, E., Verstappen, G., Gu, H., Boulting, G., Smith, Z. D., Ziller, M., Croft, G. F., Amoroso, M. W., Oakley, D. H., et al. (2011) · Cell · doi:10.1016/j.cell.2010.12.032 · Cited by 1,009 · see citing works
Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways
The Network and Pathway Analysis Subgroup of the Psychiatric Genomics Consortium (2015) · Nature Neuroscience · doi:10.1038/nn.3922 · Cited by 755 · see citing works
A general modular framework for gene set enrichment analysis
Ackermann, M., & Strimmer, K. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-47 · Cited by 358 · see citing works
Tbr1 regulates regional and laminar identity of postmitotic neurons in developing neocortex
Bedogni, F., Hodge, R. D., Elsen, G. E., Nelson, B. R., Daza, R. A. M., Beyer, R. P., Bammler, T. K., Rubenstein, J. L. R., & Hevner, R. F. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1002285107 · Cited by 322 · see citing works
Gene set analysis of genome-wide association studies: Methodological issues and perspectives
Wang, L., Jia, P., Wolfinger, R. D., Chen, X., & Zhao, Z. (2011) · Genomics · doi:10.1016/j.ygeno.2011.04.006 · Cited by 207 · see citing works
PAGER: constructing PAGs and new PAG-PAG relationships for network biology
Yue, Z., Kshirsagar, M. M., Nguyen, T., Suphavilai, C., Neylon, M. T., Zhu, L., Ratliff, T., & Chen, J. Y. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv265 · Cited by 30 · see citing works
- Authors
- Nam, D., & Kim, S.-Y.
- Affiliations
- Korea Research Institute of Bioscience and Biotechnology (KR)
- Seoul National University (KR)
- ORCID
- Nam, D.
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2008-01-11
- Volume
- 9
- Issue
- 3
- Pages
- 189-197
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- OpenAlex
- W2141703917
- Cited by
- 338
- References
- 47
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 1 · 2023: 5 · 2022: 3 · 2021: 10 · 2020: 14 · 2019: 15 · 2018: 14 · 2017: 18 · 2016: 18 · 2015: 31 · 2014: 29 · 2013: 33 · 2012: 27
- FWCI
- 2.93
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Briefings in Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- KRIBB Repository (submittedVersion)
- Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion)
- Funders
- Korea Research Council of Fundamental Science and Technology
- National Institute for Materials Science
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- gene set analysis, differential gene expression, gene expression patterns, gene groups, statistical analysis, bioinformatics software
- MeSH terms
- Algorithms, Data Interpretation, Statistical, Software, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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
