Gene set analysis for self-contained tests: Complex null and specific alternative hypotheses

Bioinformatics

Volumn 28, Issue 23, 2012, Pages 3073-3080

  Rahmatallah, Y ^a   Emmert Streib, F ^b   Glazko, G ^a  

^a UNIVERSITY OF ARKANSAS FOR MEDICAL SCIENCES   (United States)

^b QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOLOGY; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; GENETICS; HUMAN; METHODOLOGY; MULTIVARIATE ANALYSIS; NEOPLASM; STATISTICAL MODEL;

COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; HUMANS; MODELS, STATISTICAL; MULTIVARIATE ANALYSIS; NEOPLASMS;

EID: 84870441671     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bts579     Document Type: Article

References (33)

1. Ackermann, M. and Strimmer, K. (2009) A general modular framework for gene set enrichment analysis. BMC Bioinformatics, 10, 47.
2. Allison, D.B. et al. (2006) Microarray data analysis: from disarray to consolidation and consensus. Nat. Rev. Genet., 7, 55-65.
3. Baldi, P. and Long, A.D. (2001) A Bayesian framework for the analysis of micro-array expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics, 17, 509-519.
4. Baringhaus, L. and Franz, C. (2004) On a new multivariate two-sample test. J. Multivariate Anal., 88, 190-206.
5. Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Statist. Soc. B, 57, 289-300.
6. Bourgon, R. et al. (2010) Independent filtering increases detection power for high-throughput experiments. Proc. Natl Acad. Sci. USA, 107, 9546-9551.
7. Dawany, N.B. and Tozeren, A. (2010) Asymmetric microarray data produces gene lists highly predictive of research literature on multiple cancer types. BMC Bioinformatics, 11, 483.
8. Dinu, I. et al. (2007) Improving gene set analysis of microarray data by SAM-GS. BMC Bioinformatics, 8, 242.
9. Dinu, I. et al. (2009) Gene-set analysis and reduction. Brief. Bioinform., 10, 24-34.
10. Emmert-Streib, F. (2007) The chronic fatigue syndrome: a comparative pathway analysis. J. Comput. Biol., 14, 961-972.
11. Emmert-Streib, F. and Glazko, G.V. (2011) Pathway analysis of expression data: deciphering functional building blocks of complex diseases. PLoS Comput. Biol., 7, e1002053.
12. Friedman, J. and Rafsky, L. (1979) Multivariate generalization of the Wald-Wolfowitz and Smirnov two-sample tests. Ann. Stat., 7, 697-717.
13. Gatti, D.M. et al. (2010) Heading down the wrong pathway: on the influence of correlation within gene sets. BMC Genomics, 11, 574.
14. Glazko, G.V. and Emmert-Streib, F. (2009) Unite and conquer: univariate and multi-variate approaches for finding differentially expressed gene sets. Bioinformatics, 25, 2348-2354.
15. Goeman, J.J. and Buhlmann, P. (2007) Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics, 23, 980-987.
16. Hackstadt, A.J. and Hess, A.M. (2009) Filtering for increased power for microarray data analysis. BMC Bioinformatics, 10, 11.
17. Huang da, W. et al. (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res., 37, 1-13.
18. Hung, J.H. et al. (2012) Gene set enrichment analysis: performance evaluation and usage guidelines. Brief. Bioinform., 13, 281-291.
19. Jiang, Z. and Gentleman, R. (2007) Extensions to gene set enrichment. Bioinformatics, 23, 306-313.
20. Kanehisa, M. and Goto, S. (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res., 28, 27-30.
21. Klebanov, L. et al. (2007) A multivariate extension of the gene set enrichment analysis. J. Bioinform. Comput. Biol., 5, 1139-1153.
22. Kong, S.W. et al. (2006) A multivariate approach for integrating genome-wide expression data and biological knowledge. Bioinformatics, 22, 2373-2380.
23. Liu, Q. et al. (2007) Comparative evaluation of gene-set analysis methods. BMC Bioinformatics, 8, 431.
24. Lu, Y. et al. (2005) Hotelling's T2 multivariate profiling for detecting differential expression in microarrays. Bioinformatics, 21, 3105-3113.
25. Montaner, D. et al. (2009) Gene set internal coherence in the context of functional profiling. BMC Genomics, 10, 197.
26. Mootha, V.K. et al. (2003) PGC-1alpha-responsive genes involved in oxidative phos-phorylation are coordinately downregulated in human diabetes. Nat. Genet., 34, 267-273.
27. Smyth, G.K. (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol., 3, Article3.
28. Tian, L. et al. (2005) Discovering statistically significant pathways in expression profiling studies. Proc. Natl Acad. Sci. USA, 102, 13544-13549.
29. Tripathi, S. and Emmert-Streib, F. (2012) Assessment method for a power analysis to identify differentially expressed pathways. PLoS One, 7, e37510.
30. Tusher, V.G. et al. (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA, 98, 5116-5121.
31. Wang, X. et al. (2011) Linear combination test for hierarchical gene set analysis. Stat. Appl. Genet. Mol. Biol., 10, Article 13.
32. Wu, D. et al. (2010) ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics, 26, 2176-2182.
33. Xiong, H. (2006) Non-linear tests for identifying differentially expressed genes or genetic networks. Bioinformatics, 22, 919-923.