A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments

Hyungwon Choi¹ , Ronglai Shen¹ , Arul M Chinnaiyan² and Debashis Ghosh³

¹Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA

²Departments of Pathology and Urology, University of Michigan, Ann Arbor, MI, USA

³Department of Statistics and Huck Institute for Life Sciences, Penn State University, University Park, PA, USA

author email corresponding author email

BMC Bioinformatics 2007, 8:364doi:10.1186/1471-2105-8-364


Published:	27 September 2007

Abstract

Background

With the explosion in data generated using microarray technology by different investigators working on similar experiments, it is of interest to combine results across multiple studies.

Results

In this article, we describe a general probabilistic framework for combining high-throughput genomic data from several related microarray experiments using mixture models. A key feature of the model is the use of latent variables that represent quantities that can be combined across diverse platforms. We consider two methods for estimation of an index termed the probability of expression (POE). The first, reported in previous work by the authors, involves Markov Chain Monte Carlo (MCMC) techniques. The second method is a faster algorithm based on the expectation-maximization (EM) algorithm. The methods are illustrated with application to a meta-analysis of datasets for metastatic cancer.

Conclusion

The statistical methods described in the paper are available as an R package, metaArray 1.8.1, which is at Bioconductor, whose URL is http://www.bioconductor.org/ webcite.