Leveraging two-way probe-level block design for identifying differential gene expression with high-density...

To identify differentially expressed genes across experimental conditions in oligonucleotide microarray experiments, existing statistical methods commonly use a summary of probe-level expression data for each probe set and compare replicates of these values across conditions using a form of the t-test or rank sum test. Here we propose the use of a statistical method that takes advantage of the built-in redundancy architecture of high-density oligonucleotide arrays.

We employ parametric and nonparametric variants of two-way analysis of variance (ANOVA) on probe-level data to account for probe-level variation, and use the false-discovery rate (FDR) to account for simultaneous testing on thousands of genes (multiple testing problem). Using publicly available data sets, we systematically compared the performance of parametric two-way ANOVA and the nonparametric Mack-Skillings test to the t-test and Wilcoxon rank-sum test for detecting differentially expressed genes at varying levels of fold change, concentration, and sample size. Using receiver operating characteristic (ROC) curve comparisons, we observed that two-way methods with FDR control on sample sizes with 2-3 replicates exhibits the same high sensitivity and specificity as a t-test with FDR control on sample sizes with 6-9 replicates in detecting at least two-fold change.

Our results suggest that the two-way ANOVA methods using probe-level data are substantially more powerful tests for detecting differential gene expression than corresponding methods for probe-set level data.