Moderated effect size and P-value combinations for microarray meta-analyses
Tóm tắt
Motivation: With the proliferation of microarray experiments and their availability in the public domain, the use of meta-analysis methods to combine results from different studies increases. In microarray experiments, where the sample size is often limited, meta-analysis offers the possibility to considerably increase the statistical power and give more accurate results.
Results: A moderated effect size combination method was proposed and compared with other meta-analysis approaches. All methods were applied to real publicly available datasets on prostate cancer, and were compared in an extensive simulation study for various amounts of inter-study variability. Although the proposed moderated effect size combination improved already existing effect size approaches, the P-value combination was found to provide a better sensitivity and a better gene ranking than the other meta-analysis methods, while effect size methods were more conservative.
Availability: An R package metaMA is available on the CRAN.
Contact: [email protected]
Từ khóa
Tài liệu tham khảo
Choi, 2003, Combining multiple microarray studies and modeling interstudy variation, Bioinformatics, 19, i84, 10.1093/bioinformatics/btg1010
Conlon, 2007, Bayesian meta-analysis models for microarray data: a comparative study, BMC Bioinformatics, 8, 80, 10.1186/1471-2105-8-80
Hedges, 1985, Statistical Methods for Meta-Analysis.
Hedges, 1981, Distribution theory for glass's estimator of effect size and related estimators, J. Educ. Stat., 6, 107, 10.3102/10769986006002107
Hong, 2008, A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments, Bioinformatics, 24, 374, 10.1093/bioinformatics/btm620
Hu, 2006, Statistical methods for meta-analysis of microarray data: a comparative study, Inf. Syst. Front., 8, 9, 10.1007/s10796-005-6099-z
Irizarry, 2003, Exploration, normalization, and summaries of high density oligonucleotide array probe level data, Biostatistics, 4, 249, 10.1093/biostatistics/4.2.249
Jaffrézic, 2007, A structural mixed model for variances in differential gene expression studies, Genet. Res., 89, 19, 10.1017/S0016672307008646
Kulinskaya, 2007, Confidence intervals for the standardized effect arising in the comparison of two normal populations, Stat. Med., 26, 2853, 10.1002/sim.2751
LaTulippe, 2002, Comprehensive gene expression analysis of prostate cancer reveals distinct transcriptional programs associated with metastatic disease, Cancer Res., 62, 4499
Liptak, 1958, On the combination of independent tests, Magyar Tud. Akad. Mat. Kutato Int. Kzl., 3, 171
Loughin, 2004, A systematic comparison of methods for combining p-values from independent tests, Comput. Stat. Data Anal., 47, 467, 10.1016/j.csda.2003.11.020
Lusa, 2008, GeneMeta: MetaAnalysis for High Throughput Experiments, R package version 1.12.0.
Marot, 2009, Sequential analysis for microarray data based on sensitivity and meta-analysis, Stat. Appl. Genet. Mol. Biol., 8, 10.2202/1544-6115.1368
Rhodes, 2002, Meta-analysis of microarrays: Interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer, Cancer Res., 62, 4427
Scharpf, 2007, A Bayesian model for cross-study differential gene expression, Working Paper 158
Singh, 2002, Gene expression correlates of clinical prostate cancer behavior, Cancer Cell, 1, 203, 10.1016/S1535-6108(02)00030-2
Smyth, 2004, Linear models and empirical Bayes methods for assessing differential expression in microarray experiments, Stat. Appl. Genet. Mol. Biol., 3, 10.2202/1544-6115.1027
Stevens, 2005, Combining Affymetrix microarray results, BMC Bioinformatics, 6, 57, 10.1186/1471-2105-6-57
Stouffer, 1949, The American Soldier. Adjustment During Army Life
Stuart, 2004, In silico dissection of cell-type-associated patterns of gene expression in prostate cancer, Proc. Natl Acad. Sci. USA, 101, 615, 10.1073/pnas.2536479100