De-correlating expression in gene-set analysis

Bioinformatics

Volumn 26, Issue 18, 2010, Pages

  Nam, Dougu ^a  

^a Ulsan National Institute of Science and Technology   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; COMPUTER SIMULATION; EVALUATION; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORK; GENETICS; HUMAN; MALE; METABOLISM; METHODOLOGY; PROSTATE; PROSTATE TUMOR; TUMOR SUPPRESSOR GENE;

ALGORITHMS; COMPUTER SIMULATION; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; GENES, P53; HUMANS; MALE; PROSTATE; PROSTATIC NEOPLASMS;

EID: 77956524952     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btq380     Document Type: Article

References (19)

1. Ackermann, M. and Strimmer, K. (2009) A general modular framework for gene set enrichment analysis. BMC Bioinformatics, 10, 47.
2. Alter, O. et al. (2000) Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl Acad. Sci. USA, 97, 10101-10106.
3. Chandran, U.R. et al. (2007) Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process. BMC Cancer, 7, 64.
4. Dinu, I. et al. (2009) Gene-set analysis and reduction. Brief Bioinform., 10, 24-34.
5. Efron, B. and Tibshirani, R. (2007) On testing the significance of sets of genes. Ann. Appl. Stat., 1, 107-129.
6. Goeman, J.J. and Bühlmann, P. (2007) Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics, 23, 980-987.
7. Khatri, P. and Draghici, S. (2005) Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics, 21, 3587-3595.
8. Kim, S. Y. and Volsky, D. J. (2005) PAGE: parametric analysis of gene set enrichment. BMC Bioinformatics, 6, 144.
9. Mootha, V. K. et al. (2003) PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet., 34, 267-273.
10. Nam, D. and Kim, S. Y. (2008) Gene-set approach for expression pattern analysis. Brief Bioinform., 9, 189-197.
11. Newton, M. A. et al. (2007) Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis. Ann. Appl. Stat., 1, 85-106.
12. Raychaudhuri, S. et al. (2000) Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac. Symp. Biocomput., 5, 455-466.
13. Rivals, I. et al. (2007) Enrichment or depletion of a GO category within a class of genes: which test? Bioinformatics, 23, 401-407.
14. Schafer, J. and Strimmer, K. (2005) A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat. Appl. Genet. Mol. Biol., 4, Article 32.
15. Subramanian, A. et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA, 102, 15545-15550.
16. Tian, L. et al. (2005) Discovering statistically significant pathways in expression profiling studies. Proc. Natl Acad. Sci. USA, 102, 13544-13549.
17. Witten, D. M. and Tibshirani, R. (2008) Testing significance of features by lassoed principal components. Ann. Appl. Stat., 2, 986-1012.
18. Yeung, K. Y. and Ruzzo, W. L. (2001) Principal component analysis for clustering gene expression data. Bioinformatics, 17, 763-774.
19. Yu, Y. P. et al. (2004) Gene expression alterations in prostate cancer predicting tumor aggression and preceding development of malignancy. J. Clin. Oncol., 22, 2790-2799.