Using the ratio of means as the effect size measure in combining results of microarray experiments

BMC Systems Biology

Volumn 3, Issue , 2009, Pages

  Hu, Pingzhao ^a   Greenwood, Celia M T ^a,b   Beyene, Joseph ^a,b,c  

^a HOSPITAL FOR SICK CHILDREN   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

^c HOSPITAL FOR SICK CHILDREN RESEARCH INSTITUTE   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOLOGICAL MODEL; COMPUTER SIMULATION; DNA MICROARRAY; METHODOLOGY; SAMPLE SIZE; STATISTICAL ANALYSIS; STATISTICS;

COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; MODELS, BIOLOGICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; SAMPLE SIZE;

EID: 71049141822     PISSN: None     EISSN: 17520509     Source Type: Journal    
DOI: 10.1186/1752-0509-3-106     Document Type: Article

References (43)

1. Tusher V, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 2001, 98:5116-5121. 33173, 11309499, 10.1073/pnas.091062498.
2. Smyth GK. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Application in Genetics and Molecular Biology 2004, l:3.
3. 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, 286:531-536. 10.1126/science.286.5439.531, 10521349.
4. van't Veer LJ, Dai H, Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 419:624-629. 10.1038/415530a, 11823860.
5. Dudoit S, Fridlyand J, Speed TP. Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 2002, 97:77-87.
6. Xu L, Tan AC, Naiman DQ, Geman D, Winslow RL. Robust prostate cancer gene emerge from direct integration of inter-study microarray data. Bioinformatics 2005, 21:3905-3911. 10.1093/bioinformatics/bti647, 16131522.
7. Tan Y, Shi L, Tong W, Wang C. Multi-class cancer classification by total principal component regression (TPCR) using microarray gene expression data. Nucleic acids research 2005, 33:56-65. 546133, 15640445, 10.1093/nar/gki144.
8. Bloom G, Yang IV, Boulware D, Kwong KY, Coppola D, Eschrich S, Quackenbush J, Yeatman TJ. Multi-platform, multi-site, microarray-based human tumor classification. American Journal of Pathology 2004, 164:9-16. 1602228, 14695313.
9. Warnat P, Eils R, Brors B. cross-platform analysis of cancer micorarray data improves gene expression based classification of phenotypes. BMC Bioinformatics 2005, 6:265. 1312314, 16271137, 10.1186/1471-2105-6-265.
10. Cruz JA, Wishart DS. Applications of Machine Learning in Cancer Prediction and Prognosis. Cancer Informatics 2006, 2:59-78.
11. Rhodes DR, Barrette TR, Rubin MA, Ghosh D, Chinnaiyan AM. Meta-analysis of microarrays: inter-study validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Research 2002, 62:4427-4433.
12. Rhodes D, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A, Chinnaiyan A. Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci USA 2004, 101:9309-14. 438973, 15184677, 10.1073/pnas.0401994101.
13. Choi JK, Yu U, Kim S, Yoo OJ. Combining multiple microarray studies and modeling inter-study variation. Bioinformatics 2003, 19(Suppl):i84-i90.
14. Jiang H, Deng Y, Chen H, Tao L, Sha Q, Chen J, Tsai C, Zhang S. Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes. BMC Bioinformatics 2004, 5:81. 476733, 15217521, 10.1186/1471-2105-5-81.
15. Stevens JR, Doerge RW. Combining Affymetrix microarray results. BMC Bioinformatics 2005, 6:57. 1274254, 15774008, 10.1186/1471-2105-6-57.
16. Hu P, Celia GMT, Beyene J. Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models. BMC Bioinformatics 2005, 6:128. 1173085, 15921507, 10.1186/1471-2105-6-128.
17. Wang J, Do KA, Wen S, Tsavachidis S, McDonnell TJ, Logothetis CJ, Coombes KR. Merging microarray data, robust feature selection, and predicting prognosis in prostate cancer. Cancer Informatics 2006, 2:87-97.
18. Yang X, Sun X. Meta-analysis of several gene lists for distinct types of cancer: A simple way to reveal common prognostic markers. BMC Bioinformatics 2007, 8:118. 1853113, 17411443, 10.1186/1471-2105-8-118.
19. Hu P, Celia GMT, Beyene J. Integrative Analysis of Gene Expression Data Including an Assessment of Pathway enrichment for Predicting Prostate Cancer. Cancer Informatics 2006, 2:289-300.
20. Shabalin AA, Tjelmeland H, Fan C, Perou CM, Nobel AB. Merging two gene expression studies via cross platform normalization. Bioinformatics 2008, 24:1154-1160. 10.1093/bioinformatics/btn083, 18325927.
21. Hu P, Greenwood CMT, Beyene J. Statistical methods for meta-analysis of microarray data: a comparative study. Information Systems Frontiers 2006, 8:9-20.
22. DeConde RP, Hawley S, Falcon S, Clegg N, Knudsen B, Etzioni R. Combining results of microarray experiments: A rank aggregation approach. Statistical Application in Genetics and Molecular Biology 2006, 5:15.
23. DuMouchel WH, Harris JE. Bayes methods for combining the results of cancer studies in humans and other species. Journal of the American Statistical Association 1983, 78:293-315.
24. Smith TC, Spiegelhalter DJ, Thomas A. Bayesian approaches to random-effects meta-analysis: a comparative study. Stat Med 1995, 14:2685-2699. 10.1002/sim.4780142408, 8619108.
25. Cooper H, Hedges LV. The handbook of research synthesis 1994, New York: Russell Sage.
26. Hedges LV, Olkin I. Statistical methods for meta-analysis 1995, Orlando, FL: Academic Press.
27. Parmigiani G, Garrett-Mayer ES, Anbazhagan R, Gabrielson E. A cross-study comparison of gene expression studies for the molecular classification of lung cancer. Clinical Cancer Research 2004, 10:2922-2927. 10.1158/1078-0432.CCR-03-0490, 15131026.
28. Guo L, Lobenhofer EK, Wang C, Shippy R, Harris SC, Zhang L, Mei N, Chen T, Herman D, Goodsaid FM, Hurban P, Phillips KL, Xu J, Deng X, Sun YA, Tong W, Dragan YP, Shi L. Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nature Biotechnology 2006, 24:1162-1169. 10.1038/nbt1238, 17061323.
29. Shi. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nature biotechnology 2006, 24:1151-1161. 10.1038/nbt1239, 16964229.
30. Tritchler D. Modelling study quality in meta-analysis. Statistics in Medicine 1999, 18:2135-2145. 10.1002/(SICI)1097-0258(19990830)18:16<2135::AID-SIM183>3.0.CO;2-5, 10441769.
31. Hu P, Beyene J, Greenwood CMT. Tests for differential gene expression using weights in oligonucleotide microarray experiments. BMC Genomics 2006, 8:9-20.
32. Heber S, Sick B. Quality assessment of Affymetrix GeneChip data. OMICS: A Journal of Integrative Biology 2006, 10:358-368. 10.1089/omi.2006.10.358, 17069513.
33. Affymetrix -- Technical Manual. , http://www.affymetrix.com/support/technical/manual/expression_manual.aff x
34. Oehlert GW. A Note on the delta method. The American Statistician 1992, 46:27-29.
35. 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, 85:289-300.
36. Wu Z, Irizarry RA, Gentleman R, Martinez MF, Spencer F. A Model Based Background Adjustement for Oligonucleotide Expression Arrays. Journal of the American Statistical Association 2004, 99:909-915.
37. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982, 143:29-36.
38. Welsh JB, Sapinoso LM, Su AI, Kern SG, Wang J. Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. Cancer Research 2001, 61:5974-5978.
39. Singh D, Febbo PG, Ross K, Jackson DG, Manola J, Ladd C, Tamayo P, Renshaw AA, D'Amico AV, Richie JP, Lander ES, Loda M, Kantoff PW, Golub TR, Sellers WR. Gene expression correlates of clinical prostate cancer behaviour. Cancer Cell 2002, 1:203-209. 10.1016/S1535-6108(02)00030-2, 12086878.
40. LaTulippe E, Satagopan J, Smith A, Scher H, Scardino P, Reuter V, Gerald WL. Comprehensive gene expression analysis of prostate cancer reveals distinct transcriptional programs associated with metastatic disease. Cancer Research 2002, 62:4499-4506.
41. Stuart RO, Wachsman W, Berry CC, Wang-Rodriguez J, Wasserman L, Klacansky I, Masys D, Arden K, Goodison S, McClelland M, Wang Y, Sawyers A, Kalcheva I, Tarin D, Mercola D. In silico dissection of cell-type-associated patterns of gene expression in prostate cancer. Proc Natl Acad Sci USA 2004, 101:615-620. 327196, 14722351, 10.1073/pnas.2536479100.
42. Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Research 2003, 31:E15. 150247, 12582260, 10.1093/nar/gng015.
43. Tricoli JV, Schoenfeldt M, Conley BA. Detection of prostate cancer and predicting progression: current and future diagnostic markers. Clinical cancer research 2004, 10:3943-3953. 10.1158/1078-0432.CCR-03-0200, 15217924.