Cost-effective Screening for Differentially Expressed Genes in Microarray Experiments Based on Normal Mixtures
DOI:
https://doi.org/10.17713/ajs.v32i3.458Abstract
Microarray experiments allow the monitoring of expression levels for thousands of genes simultaneously. Based on data obtained from the co-hybridization of two mRNA samples, a frequent goal is to find out which genes are differentially expressed. For this purpose, we propose to estimate the distribution of popular test statistics by a mixture of normal distributions. These statistics are calculated for each gene separately. A Bayes classifier is then used to decide upon differential expression. The cut-off for the classifier is chosen according to the number of false positives and negatives when applied to realistic data generating models. In particular, we generate data from a mixture model and from an Empirical Bayes model. By comparing the numbers of false decisions for various test statistics in the context of the considered models, we investigate which of the statistics are particularly suitable with our approach.
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