A Bayesian Mixture Model for Metaanalysis of Microarray Studies

Overview

Journal Funct Integr Genomics

Publisher Springer

Specialties Genetics
Molecular Biology

Date 2007 Sep 20

PMID 17879102

Citations 6

Authors

Erin M Conlon

Affiliations

Soon will be listed here.

Abstract

The increased availability of microarray data has been calling for statistical methods to integrate findings across studies. A common goal of microarray analysis is to determine differentially expressed genes between two conditions, such as treatment vs control. A recent Bayesian metaanalysis model used a prior distribution for the mean log-expression ratios that was a mixture of two normal distributions. This model centered the prior distribution of differential expression at zero, and separated genes into two groups only: expressed and nonexpressed. Here, we introduce a Bayesian three-component truncated normal mixture prior model that more flexibly assigns prior distributions to the differentially expressed genes and produces three groups of genes: up and downregulated, and nonexpressed. We found in simulations of two and five studies that the three-component model outperformed the two-component model using three comparison measures. When analyzing biological data of Bacillus subtilis, we found that the three-component model discovered more genes and omitted fewer genes for the same levels of posterior probability of differential expression than the two-component model, and discovered more genes for fixed thresholds of Bayesian false discovery. We assumed that the data sets were produced from the same microarray platform and were prescaled.

Citing Articles

On integrating multi-experiment microarray data.

Tsiliki G, Vlachakis D, Kossida S Philos Trans A Math Phys Eng Sci. 2014; 372(2016):20130136.

PMID: 24751870 PMC: 3996576. DOI: 10.1098/rsta.2013.0136.

Consistent Differential Expression Pattern (CDEP) on microarray to identify genes related to metastatic behavior.

Tsoi L, Qin T, Slate E, Zheng W BMC Bioinformatics. 2011; 12:438.

PMID: 22078224 PMC: 3251006. DOI: 10.1186/1471-2105-12-438.

Conceptual aspects of large meta-analyses with publicly available microarray data: a case study in oncology.

Schmidberger M, Lennert S, Mansmann U Bioinform Biol Insights. 2011; 5:13-39.

PMID: 21423405 PMC: 3045047. DOI: 10.4137/BBI.S5537.

A Bayesian model for cross-study differential gene expression.

Scharpf R, Tjelmeland H, Parmigiani G, Nobel A J Am Stat Assoc. 2010; 104(488):1295-1310.

PMID: 21127725 PMC: 2994029. DOI: 10.1198/jasa.2009.ap07611.

Candidate pathways and genes for prostate cancer: a meta-analysis of gene expression data.

Gorlov I, Byun J, Gorlova O, Aparicio A, Efstathiou E, Logothetis C BMC Med Genomics. 2009; 2:48.

PMID: 19653896 PMC: 2731785. DOI: 10.1186/1755-8794-2-48.

References

Mah N, Thelin A, Lu T, Nikolaus S, Kuhbacher T, Gurbuz Y . A comparison of oligonucleotide and cDNA-based microarray systems. Physiol Genomics. 2003; 16(3):361-70. DOI: 10.1152/physiolgenomics.00080.2003. View

Shen R, Ghosh D, Chinnaiyan A . Prognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data. BMC Genomics. 2004; 5(1):94. PMC: 544889. DOI: 10.1186/1471-2164-5-94. View

Rhodes D, Barrette T, Rubin M, Ghosh D, Chinnaiyan A . Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Res. 2002; 62(15):4427-33. View

Broet P, Richardson S, Radvanyi F . Bayesian hierarchical model for identifying changes in gene expression from microarray experiments. J Comput Biol. 2002; 9(4):671-83. DOI: 10.1089/106652702760277381. View

Tweedie R, Scott D, Biggerstaff B, Mengersen K . Bayesian meta-analysis, with application to studies of ETS and lung cancer. Lung Cancer. 1996; 14 Suppl 1:S171-94. DOI: 10.1016/s0169-5002(96)90222-6. View

Ghosh D, Barette T, Rhodes D, Chinnaiyan A . Statistical issues and methods for meta-analysis of microarray data: a case study in prostate cancer. Funct Integr Genomics. 2003; 3(4):180-8. DOI: 10.1007/s10142-003-0087-5. View

Jarvinen A, Hautaniemi S, Edgren H, Auvinen P, Saarela J, Kallioniemi O . Are data from different gene expression microarray platforms comparable?. Genomics. 2004; 83(6):1164-8. DOI: 10.1016/j.ygeno.2004.01.004. View

Kendziorski C, Newton M, Lan H, Gould M . On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Stat Med. 2003; 22(24):3899-914. DOI: 10.1002/sim.1548. View

Warnat P, Eils R, Brors B . Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes. BMC Bioinformatics. 2005; 6:265. PMC: 1312314. DOI: 10.1186/1471-2105-6-265. View

10.

Hu J, Zou F, Wright F . Practical FDR-based sample size calculations in microarray experiments. Bioinformatics. 2005; 21(15):3264-72. DOI: 10.1093/bioinformatics/bti519. View

11.

Wang J, Coombes K, Highsmith W, Keating M, Abruzzo L . Differences in gene expression between B-cell chronic lymphocytic leukemia and normal B cells: a meta-analysis of three microarray studies. Bioinformatics. 2004; 20(17):3166-78. DOI: 10.1093/bioinformatics/bth381. View

12.

Stevens J, Doerge R . Combining Affymetrix microarray results. BMC Bioinformatics. 2005; 6:57. PMC: 1274254. DOI: 10.1186/1471-2105-6-57. View

13.

Baldi P, Long A . A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes. Bioinformatics. 2001; 17(6):509-19. DOI: 10.1093/bioinformatics/17.6.509. View

14.

Park T, Yi S, Shin Y, Lee S . Combining multiple microarrays in the presence of controlling variables. Bioinformatics. 2006; 22(14):1682-9. DOI: 10.1093/bioinformatics/btl183. View

15.

Newton M, Noueiry A, Sarkar D, Ahlquist P . Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics. 2004; 5(2):155-76. DOI: 10.1093/biostatistics/5.2.155. View

16.

Bhowmick D, Davison A, Goldstein D, Ruffieux Y . A Laplace mixture model for identification of differential expression in microarray experiments. Biostatistics. 2006; 7(4):630-41. DOI: 10.1093/biostatistics/kxj032. View

17.

Conlon E, Song J, Liu A . Bayesian meta-analysis models for microarray data: a comparative study. BMC Bioinformatics. 2007; 8:80. PMC: 1851021. DOI: 10.1186/1471-2105-8-80. View

18.

Townsend J, Hartl D . Bayesian analysis of gene expression levels: statistical quantification of relative mRNA level across multiple strains or treatments. Genome Biol. 2003; 3(12):RESEARCH0071. PMC: 151173. DOI: 10.1186/gb-2002-3-12-research0071. View

19.

Conlon E, Song J, Liu J . Bayesian models for pooling microarray studies with multiple sources of replications. BMC Bioinformatics. 2006; 7:247. PMC: 1534062. DOI: 10.1186/1471-2105-7-247. View

20.

Parmigiani G, Garrett-Mayer E, Anbazhagan R, Gabrielson E . A cross-study comparison of gene expression studies for the molecular classification of lung cancer. Clin Cancer Res. 2004; 10(9):2922-7. DOI: 10.1158/1078-0432.ccr-03-0490. View