Bayesian meta-analysis models for microarray data: a comparative study

Background: With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods. Results: Two Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce the gene-specific posterior probability of differential expression, which is the basis for inference. Since the standardized expression integration model includes inter-study variability, it may improve accuracy of results versu...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Erin M. Conlon, Joon J. Song, Anna Liu
• Keywords: Bayesian Meta-analysis Models, BMCBI 2007, Gene Expression, Meta-analysis