Semi-supervised Significance Score of Differential Gene Expressions

Abstract

AbstractIn gene expression analyses for DNA microarray data, various statistical scores have been proposed for evaluating significance of genes exhibiting differential expression between two or more controlled conditions. To consider an unsupervised case or a semi-supervised case rather than a well-studied supervised case, we assume a latent variable model and apply the optimal discovery procedure (ODP) proposed by Storey (2005) to the model. Theoretical consideration leads to two different interpretations of the hidden variable, i.e., it only implicitly affects the alternative model through the model parameters, or is explicitly included in the alternative model, so that they correspond to two different implementations of ODP. By comparing the two implementations through experiments with simulation data, we found that sharing the latent variable estimation as in the latter case is effective in increasing the detectability of truly active genes. We also propose unsupervised and semi-supervised rating of genes and show its effectiveness as a significance score.