Assessing differential gene expression with small sample sizes in oligonucleotide arrays using a mean-variance model.

The identification of the genes that are differentially expressed in two-sample microarray experiments remains a difficult problem when the number of arrays is very small. We discuss the implications of using ordinary t-statistics and examine other commonly used variants. For oligonucleotide arrays with multiple probes per gene, we introduce a simple model relating the mean and variance of expression, possibly with gene-specific random effects. Parameter estimates from the model have natural shrinkage properties that guard against inappropriately small variance estimates, and the model is used to obtain a differential expression statistic. A limiting value to the positive false discovery rate (pFDR) for ordinary t-tests provides motivation for our use of the data structure to improve variance estimates. Our approach performs well compared to other proposed approaches in terms of the false discovery rate.