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

Background: 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. Results: 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...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Leah Barrera, Chris Benner, Yong-Chuan Tao, Elizabeth A. Winzeler, Yingyao Zhou
• Keywords: BMCBI 2004, Expressed Genes, Sample Sizes, Statistical Method