Abstract
With the proliferation of related microarray studies by independent groups, a natural step in the analysis of these gene expression data is to combine the results across these studies. However, this raises a variety of issues in the analysis of such data. In this article, we discuss the statistical issues of combining data from multiple gene expression studies. This leads to more complications than those in standard meta-analyses, including different experimental platforms, duplicate spots and complex data structures. We illustrate these ideas using data from four prostate cancer profiling studies. In addition, we develop a simple approach for assessing differential expression using the LASSO method. A combination of the results and the pathway databases are then used to generate candidate biological pathways for cancer.
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References
Benjamini Y, Hochberg Y (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300
Benjamini Y, Yekutieli D (2002) The control of the false discovery rate in multiple testing under dependency. Ann Stat 30:1165–1188
Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M (2001) Minimum information about a microarray experiment (MIAME)—toward standards for microarray data. Nat Genet 29:365–371
Bussemaker H, Li H, Siggia ES (2001) Regulatory element detection using correlation with expression. Nat Genet 27(2):167–171
Dhanasekaran SM, Barrette TR, Ghosh D, Shah R, Varambally S, Kurachi K, Pienta KJ, Rubin MA, Chinnaiyan AM (2001) Delineation of prognostic biomarkers in prostate cancer. Nature 412:822–826
Efron B, Tibshirani R (2002) Empirical Bayes methods and false discovery rates for microarrays. Genet Epidemiol 23:70--86
Efron B, Tibshirani R, Storey JD, Tusher V (2001) Empirical Bayes analysis of a microarray experiment. J Am Stat Assoc 96:1151–1160
Ibrahim JG, Chen M-H, Gray RJ (2002) Bayesian models for gene expression with DNA microarray data. J Am Stat Assoc 97:88--99
Irizarry K, Kustanovich V, Li C, Brown N, Nelson S, Wong W, Lee CJ (2000) Genome-wide analysis of single-nucleotide polymorphisms in human expressed sequences. Nat Genet 26:233–236
Kuo WP, Jenssen T-K, Butte AJ, Ohno-Machado L, Kohane IS (2002) Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics 18:405--412
Lipshutz RJ, Fodor SPA, Gingeras TR, Lockhart DJ (1999) High density synthetic oligonucleotide arrays. Nat Genet S21:20–24
Little RJA, Rubin DB (1987) Statistical analysis with missing data. Wiley, New York
Lonnestedt I, Speed TP (2002) Replicated microarray data. Stat Sin 12:31--46
Luo J, Duggan DJ, Chen Y, Sauvageot J, Ewing CM, Bittner ML, Trent JM, Isaacs WB (2001) Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling. Cancer Res 61:4683--4688
Magee JA, Araki T, Patil S, Ehrig T, True L, Humphrey PA, Catalona WJ, Watson MA, Milbrandt J (2001) Expression profiling reveals hepsin overexpression in prostate cancer. Cancer Res 61:5692--5696
Normand SL (1999) Meta-analysis: formulating, evaluating, combining and reporting. Stat Med 18:321–359
Rhodes D, Barrette TR, Rubin MA, Ghosh D, Chinnaiyan AM (2002) Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Res 62:4427–4433
Schena M (2000) Microarray biochip technology. Eaton, Sunnyvale, Calif.
Shaffer J (1995) Multiple hypothesis testing. Annu Rev Psychol 46:561–584
Storey JD (2002) A direct approach to false discovery rates. J R Stat Soc B 64:479–495
Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM (1999) Systematic determination of genetic network architecture. Nature Genet 22:281--285
Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc B 58:267–288
Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to ionization radiation response. Proc Natl Acad Sci 98:5116--5121
Welsh JB, Sapinoso LM, Su AI, Kern SG, Wang-Rodriguez J, Moskaluk CA, Frierson HF Jr, Hampton GM (2001) Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. Cancer Res 61:5974–5978
Acknowledgements
The first author would like to thank Sharon Sun and Xihong Lin for useful discussions. The work of D.G. has been partially supported by a Prostate Cancer Seed Grant and MUNN Idea Grant from the University of Michigan Cancer Center.
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Ghosh, D., Barette, T.R., Rhodes, D. et al. Statistical issues and methods for meta-analysis of microarray data: a case study in prostate cancer. Funct Integr Genomics 3, 180–188 (2003). https://doi.org/10.1007/s10142-003-0087-5
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DOI: https://doi.org/10.1007/s10142-003-0087-5