Empirical Bayes microarray ANOVA and grouping cell lines by equal expression levels

In the exploding field of gene expression techniques such as DNA microarrays, there are still few general probabilistic methods for analysis of variance. Linear models and ANOVA are heavily used tools in many other disciplines of scientific research. The usual F-statistic is unsatisfactory for microarray data, which explore many thousand genes in parallel, with few replicates. We present three potential one-way ANOVA statistics in a parametric statistical framework. The aim is to separate genes that are differently regulated across several treatment conditions from those with equal regulation. The statistics have different features and are evaluated using both real and simulated data. Our statistic B-1 generally shows the best performance, and is extended for use in an algorithm that groups cell lines by equal expression levels for each gene. An extension is also outlined for more general ANOVA tests including several factors. The methods presented are implemented in the freely available statistical language R. They are available at http://www.math.uu.se/staff/pages/?uname=ingrid.