Mixture-model based estimation of gene expression variance from public database improves identification of differentially expressed genes in small sized microarray data.

ABSTRACT:

Motivation

The small number of samples in many microarray experiments is a challenge for the correct identification of differentially expressed gens (DEGs) by conventional statistical means. Information from public microarray databases can help more efficient identification of DEGs. To model various experimental conditions of a public microarray database, we applied Gaussian mixture model and extracted bi- or tri-modal distributions of gene expression. Prior variance of Baldi's Bayesian framework was estimate for the analysis of the small sample-sized datasets.

Results

First, we estimated the prior variance of a gene expression by pooling variances obtained from mixture modeling of large samples in the public microarray database. Then, using the prior variance, we identified

SUBMITTER: Kim M 

PROVIDER: S-EPMC2820675 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature