Bayesian models for pooling microarray studies with multiple sources of replications

Background: Biologists often conduct multiple but different cDNA microarray studies that all target the same biological system or pathway. Within each study, replicate slides within repeated identical experiments are often produced. Pooling information across studies can help more accurately identify true target genes. Here, we introduce a method to integrate multiple independent studies efficiently. Results: We introduce a Bayesian hierarchical model to pool cDNA microarray data across multiple independent studies to identify highly expressed genes. Each study has multiple sources of variation, i.e. replicate slides within repeated identical experiments. Our model produces the genespecific posterior probability of differential expression, which provides a direct method for ranking genes, and provides Bayesian estimates of false discovery rates (FDR). In simulations combining two and five independent studies, with fixed FDR levels, we observed large increases in the number of discover...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Erin M. Conlon, Joon J. Song, Jun S. Liu
• Keywords: BMCBI 2006, CDNA Microarray, Independent Studies, Multiple Independent Studies