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

ABSTRACT:

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-specifi

SUBMITTER: Conlon EM 

PROVIDER: S-EPMC1851021 | biostudies-literature | 2007 Mar

REPOSITORIES: biostudies-literature