Gene set analysis methods: Statistical models and methodological differences

Briefings in Bioinformatics

Volumn 15, Issue 4, 2014, Pages 504-518

  Maciejewski, Henryk ^a  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Competitive methods; Gene expression; Gene set analysis; GWAS; High throughput data; Self contained methods

Indexed keywords

GENETIC ASSOCIATION; STATISTICAL MODEL; VALIDATION STUDY;

GENOME-WIDE ASSOCIATION STUDY; MODELS, STATISTICAL;

EID: 84904723024     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbt002     Document Type: Article

References (27)

1. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005;102(43):15545-50.
2. Ein-Dor L, Kela I, Getz G, et al. Outcome signature genes in breast cancer: is there a unique set? Bioinformatics 2005;21(2):171-8.
3. Ein-Dor L, Yuk O, Domany E. Thousands of samples are needed to generate a robust gene list for predicting outcome of cancer. Proc Natl Acad Sci USA 2006;103(15):5923-28.
4. Nam D, Kim SY. Gene-set approach for expression pattern analysis. Brief Bioinform 2008;9(3):189-97.
5. Goeman JJ, van de Geer SA, de Kort F, et al. A global test for groups of genes: testing association with clinical outcome. Bioinformatics 2004;20(1):93-9.
6. Goeman JJ, Oosting J, Cleton-Jansen AM, et al. Testing association of a pathway with survival using gene expression data. Bioinformatics 2005;21(9):1950-7.
7. Mansmann U, Meister R. Testing differential gene expression in functional groups-Goeman's global test versus an ANCOVA approach. Methods Inf Med 2005;44:449-53.
8. Fridley BL, Jenkins GD, Biernacka JM. Self-contained gene-set analysis of expression data: an evaluation of existing and novel methods. PLoS One 2010;5(9):e12693.
9. Efron B, Tibshirani R. On testing the significance of sets of genes. Ann Appl Stat 2007;1(1):107-29.
10. Barry WT, Nobel AB, Wright FA. Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics 2005;21(9):1943-9.
11. Newton MA, Quintana FA, den Boon JA. Random set methods identify distinct aspects of the enrichment signal in gene-set analysis. Ann Appl Stat 2007;1(1):85-106.
12. Goeman JJ, Buehlmann P. Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics 2007;23(8):980-7.
13. Pavlidis P, Lewis DP, Noble WS. Exploring gene expression data with class scores. Pac Symp Biocomput 2002;7:474-85.
14. Tian L, Greenberg SA, Kong SW, et al. Discovering statistically significant pathways in expression profiling studies. Proc Natl Acad Sci USA 2005;102(38):13544-9.
15. Kim SY, Volsky DJ. PAGE: parametric analysis of gene set enrichment. BMC Bioinformatics 2005;6-144.
16. Irizarry RA, Wang C, Zhou Y, et al. Gene set enrichment analysis made simple. Stat Methods Med Res 2009;18(6):565-75.
17. Liu Q, Dinu I, Adewale AJ, et al. Comparative evaluation of gene set analysis methods. BMC Bioinformatics 2007;8:431.
18. Song S, Black MA. Microarray-based gene set analysis: a comparison of current methods. BMC Bioinformatics 2008;9:502.
19. Dinu I, Liu Q, Potter JD, et al. A biological evaluation of six gene set analysis methods for identification of differentially expressed pathways in microarray data. Cancer Inform 2008;6:357-68.
20. Wu MC, Lin X. Prior biological knowledge-based approaches for the analysis of genome-wide expression profiles using gene sets and pathways. Stat Methods Med Res 2009;18(6):577-93.
21. Ackermann M, Strimmer K. A general modular framework for gene set enrichment analysis. BMC Bioinformatics 2009;10:47.
22. Hung JH, Yang TH, Hu Z, et al. Gene set enrichment analysis: performance evaluation and usage guidelines. Brief Bioinform 2012;13(3):281-91.
23. Mootha VK, Lindgren CM, Eriksson KF, et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 2003;34:267-73.
24. Pavlidis P, Qin J, Arango V, et al. Using the gene ontology for microarray data mining: a comparison of methods and application to age effect in human prefrontal cortex. Neurochem Res 2004;29(6):1213-22.
25. Jiang Y, Gentleman R. Extensions to gene set enrichment. Bioinformatics 2007;23(3):306-13.
26. Dinu I, Potter JD, Mueller T, et al. Improving gene set analysis of microarray data by SAM-GS. BMC Bioinformatics 2007;8:242.
27. Kong SW, Pu WT, Park PJ. A multivariate approach for integrating genome-wide expression data and biological knowledge. Bioinformatics 2006;22(19):2373-80.