Comparative analysis of gene sets in the gene ontology space under the multiple hypothesis testing framework

Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004

Volumn , Issue , 2004, Pages 425-435

  Zhong, Sheng ^a   Tian, Lu ^a   Li, Cheng ^a,b   Storch, Kai Florian ^c   Wong, Wing H ^a  

^a HARVARD UNIVERSITY   (United States)

^b DANA FARBER CANCER INSTITUTE   (United States)

^c HARVARD MEDICAL SCHOOL   (United States)

Author keywords

Data Mining; False Discovery Rate; Gene Ontology; GoSurfer; Microarray; Q value; Visualization

Indexed keywords

COMPUTER SOFTWARE; CORRELATION METHODS; CYTOLOGY; DATA MINING; DATABASE SYSTEMS; FUNCTIONS; GENETIC ENGINEERING; SOFTWARE ENGINEERING; STATISTICAL TESTS; THROUGHPUT; TUMORS; VISUALIZATION;

COMPUTER SOFTWARE - GOSURFER; FALSE DISCOVERY RATE (FDR); GENE ONTOLOGY (GO); MICROARRAYS; Q-VALUE;

GENES;

EID: 14044276956     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

1. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25, 25-9 (2000).
2. Genter, M.B. et al. Microarray-based discovery of highly expressed olfactory mucosal genes: potential roles in the various functions of the olfactory system. Physiol Genomics 16, 67-81 (2003).
3. Mazzolini, G. et al. Pancreatic cancer escape variants that evade immunogene therapy through loss of sensitivity to IFNgamma-induced apoptosis. Gene Ther 10, 1067-78 (2003).
4. Berriz, G.F., King, O.D., Bryant, B., Sander, C. & Roth, F.P. Characterizing gene sets with FuncAssociate. Bioinformatics 19, 2502-4 (2003).
5. Castillo-Davis, C.I. & Hard, D.L. GeneMerge - post-genomic analysis, data mining, and hypothesis testing. Bioinformatics 19, 891-2 (2003).
6. Zeeberg, B.R. et al. GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol 4, R28 (2003).
7. Zhang, B., Schmoyer, D., Kirov, S. & Snoddy, J. GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies. BMC Bioinformatics 5, 16 (2004).
8. Doniger, S.W. et al. MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol 4, R7 (2003).
9. Al-Shahrour, F., Diaz-Uriarte, R. & Dopazo, J. FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics 20, 578-80 (2004).
10. Storey JD. The positive false discovery rate: A Bayesian interpretation and the q-value. Annals of Statistics 31: 2013-2035 (2003)
11. Storey, J.D. & Tibshirani, R. Statistical significance for genomewide studies. Proc Natl Acad Sci USA 100, 9440-5 (2003).
12. Van der Laan, M. & Bryan, J. Gene expression analysis with the parametric bootstrap, Biostatistics, 2, 445-461 (2001)
13. Reiner, A., Yekutieli, D. & Benjamini, Y. Identifying differentially expressed genes using false discovery rate controlling procedures, Bioinfomatics, 19, 368-375 (2003)
14. Efron, B., Tibshirani, R., Storey, J.D., & Tusher, V. Empirical Bayes analysis of a microarray experiment. Journal of the American Statistical Association, 96, 1151-1160 (2001)
15. Ge, Y., Dudoit, S. & Speed, T. P. Resampling-based multiple testing for microarray data analysis, Sociedad Espanola de Estadistica e Investigation Operative. Test, 12, 1-77 (2003)
16. Storey, J. D. A direct approach to false discovery rates. Journal of the Royal Statistical Society, Series B, 64, 479-498 (2002)
17. Storey J.D., Tibshirani R. SAM thresholding and false discovery rates for detecting differential gene expression in DNA microarrays. The Analysis of Gene Expression Data: Methods and Software. Springer, New York (2003)
18. Storey, J.D., Taylor, J.E. & Siegmund, D. Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach. Journal of the Royal Statistical Society B 66, 187-198 (2004).
19. Zhong S, Storch F, Lipan O, Kao MJ, Weitz C, Wong WH. GoSurfer: a graphical interactive tool for comparative analysis of large gene sets in Gene Ontology space. Applied Bioinformatics 3(3) In press.
20. Li, C. & Wong, W.H. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci USA 98, 31-6 (2001).
21. Singh, D. et al. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1, 203-9 (2002).
22. Dhanasekaran, S.M. et al. Delineation of prognostic biomarkers in prostate cancer. Nature 412, 822-6 (2001).
23. Hanahan, D. & Weinberg, R.A. The hallmarks of cancer. Cell 100, 57-70 (2000).
24. Tusher, V.G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98, 5116-21 (2001).
25. Storey J.D. A direct approach to false discovery rate. Journal of Royal Statistical Society B 64: 479-498
26. Westfall, P.H., Young, S.S. Rsampling-based multiple testing: examples and methods for p-value adjustment. John Wiley & Sons, Inc., New York (1993)
27. http://llama.med.harvard.edu/FuncAssociate_Methods.html
28. Dudoit, S., Shaffer, J.P., Boldrick, J.C. Multiple Hypothesis Testing in Microarray Experiments. Statistical Science 18: 71-103 (2003)
29. Westfall, P.H., Young, S.S. Rsampling-based multiple testing: examples and methods for p-value adjustment. John Wiley & Sons, Inc., New York (1993)
30. http://llama.med.harvard.edu/FuncAssociate_Methods.html
31. Benjamini, Y. & Hocheberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society B, 57, 289-300 (1995).
32. Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple hypothesis testing under dependency. 1165-1188 (2001).