A comparative study on gene-set analysis methods for assessing differential expression associated with the survival phenotype

BMC Bioinformatics

Volumn 12, Issue , 2011, Pages

  Lee, Seungyeoun ^a   Kim, Jinheum ^b   Lee, Sunho ^a  

^a Sejong University   (South Korea)

^b Suwon University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

COMPARATIVE STUDIES; CORRELATION STRUCTURE; DIFFERENTIAL EXPRESSIONS; GENE SET ENRICHMENT ANALYSIS; KOLMOGOROV-SMIRNOV TEST; SIGNALING PATHWAYS; SYNERGISTIC EFFECT; TRANSENDOTHELIAL MIGRATIONS;

COMPUTER SIMULATION; DISEASES; TESTING;

GENE EXPRESSION;

ARTICLE; COMPUTER SIMULATION; DNA MICROARRAY; FEMALE; GENE EXPRESSION PROFILING; GENETICS; HUMAN; METHODOLOGY; MORTALITY; OVARY TUMOR; PHENOTYPE; SURVIVAL;

COMPUTER SIMULATION; FEMALE; GENE EXPRESSION PROFILING; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; OVARIAN NEOPLASMS; PHENOTYPE; RESEARCH DESIGN; SURVIVAL ANALYSIS;

EID: 80053352806     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-12-377     Document Type: Article

References (24)

1. Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E, Houstis N, Daly MJ, Patterson N, Mesirov JP, Golub TR, Tamayo P, Speigelman B, Lander ES, Hirschhorn JN, Altshuler D, Groop LC. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately down regulated in human diabetes. Nat Genet 2003, 34:267-273. 10.1038/ng1180, 12808457.
2. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci 2005, 102:15545-15550. 10.1073/pnas.0506580102, 1239896, 16199517.
3. Kim SY, Volsky DJ. PAGE: Parametric analysis of gene set enrichment. BMC Bioinformatics 2005, 6:144. 10.1186/1471-2105-6-14, 549200, 15663789.
4. Dinu I, Potter JD, Mueller T, Liu Q, Adewale AJ, Jhangri GS, Einecke G, Famulski KS, Halloran P, Yasui Y. Improving gene set analysis of microarray data by SAM-GS. BMC Bioinformatics 2007, 8:242. 10.1186/1471-2105-8-242, 1931607, 17612399.
5. Goeman JJ, van de Geer SA, de Kort F, van Houwelingen HC. A global test for groups of genes: Testing association with a clinical outcome. Bioinformatics 2004, 20:93-99. 10.1093/bioinformatics/btg382, 14693814.
6. Goeman JJ, Oosting J, Cleton-Jansen AM, Anninga JK, van Houwelingen HC. Testing association of a pathway with survival using gene expression data. Bioinformatics 2005, 21:1950-1957. 10.1093/bioinformatics/bti267, 15657105.
7. Binder H, Schumacher M. Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models. BMC Bioinformatics 2008, 9:14. 10.1186/1471-2105-9-14, 2245904, 18186927.
8. Boulesteix AL, Hothorn T. Testing the additional predictive value of high-dimensional molecular data. BMC Bioinformatics 2010, 11:78. 10.1186/1471-2105-11-78, 2837029, 20144191.
9. Adewale AJ, Dinu I, Potter JD, Liu Q, Yasui Y. Pathway analysis of microarray data via regression. J of Comp Biology 2008, 15(3):269-277.
10. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, et al. Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature 2000, 403(6769):503-511. 10.1038/35000501, 10676951.
11. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286:531-537. 10.1126/science.286.5439.531, 10521349.
12. Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI, Gascoyne RD, Muller-Hermelink KM, Smeland EB, Staudt LM. The use of molecular profiling to predict survival after chemotherapy for diffuse large B-cell lymphoma. The New England Journal of Medicine 2002, 346(25):1937-1947. 10.1056/NEJMoa012914, 12075054.
13. Gui J, Li HZ. Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics 2005, 21:3001-3008. 10.1093/bioinformatics/bti422, 15814556.
14. Tibshirani R. The Lasso method for variable selection in the Cox model. Statistics in Medicine 1997, 16:385-395. 10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3, 9044528.
15. Tian L, Greenberg SA, Kong SW, Altschuler J, Kohane IS, Park PJ. Discovering statistically significant pathways in expression profiling studies. Proc Natl Acad Sci USA 2005, 102:13544-13549. 10.1073/pnas.0506577102, 1200092, 16174746.
16. Bair E, Tibshirani R. Semi-supervised methods to predict patient survival from gene downloaded from gene expression data. PLoS Biology 2004, 2(4):511-522.
17. Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000, 16(10):906-914. 10.1093/bioinformatics/16.10.906, 11120680.
18. Chen X, Wang L. Integrating Biological Knowledge with Gene Expression Profiles for Survival Prediction of Cancer. J of Comp Biology 2009, 16(20):265-278.
19. Liu Q, Dinu I, Adewale AJ, Potter JD, Yasui Y. Comparative evaluation of gene-set analysis methods. BMC Bioinformatics 2007, 8:431. 10.1186/1471-2105-8-431, 2238724, 17988400.
20. Jung K, Becker B, Brunner E, Beiβbarth T. Comparison of global tests for functional gene sets in two-group designs and selection of potentially effect-causing genes. Bioinformatics 2011, 27:1377-1383. 10.1093/bioinformatics/btr152, 21441576.
21. Dressman HK, Berchuck A, Chan G, Zhai J, Bild A, Sayer R, Cragun J, Clarke J, Whitaker RS, Li LH, Gray J, Marks J, Ginsburg GS, Potti A, West M, Nevins JR, Lancaster JM. An integrated genomic-based approach to individualized treatment of patients with advanced-stage ovarian cancer. J Clin Oncol 2007, 25:517-525. 10.1200/JCO.2006.06.3743, 17290060.
22. Crijns AP, Fehrmann RS, de Jong S, Gerbens F, Meersma GJ, Klip HG, Hollema H, Hofstra RM, te Meerman GJ, de Vries EG, van der Zee AGJ. Survival-related profile, pathways, and transcription factors in ovarian cancer. PLoS Med 2009, 6:e1000024.
23. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B 1995, 57:289-300.
24. Goeman JJ, Bühlmann P. Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics 2007, 23:980-987. 10.1093/bioinformatics/btm051, 17303618.