Detecting multivariate differentially expressed genes
1 Computational Biology, Department of Physics, Linköping University, SE-581 53 Linköping, Sweden
2 Unit of Computational Medicine, King Gustaf V Research Institute, Department of Medicine, Karolinska Institutet, SE-171 76 Stockholm, Sweden
BMC Bioinformatics 2007, 8:150 doi:10.1186/1471-2105-8-150Published: 9 May 2007
Gene expression is governed by complex networks, and differences in expression patterns between distinct biological conditions may therefore be complex and multivariate in nature. Yet, current statistical methods for detecting differential expression merely consider the univariate difference in expression level of each gene in isolation, thus potentially neglecting many genes of biological importance.
We have developed a novel algorithm for detecting multivariate expression patterns, named Recursive Independence Test (RIT). This algorithm generalizes differential expression testing to more complex expression patterns, while still including genes found by the univariate approach. We prove that RIT is consistent and controls error rates for small sample sizes. Simulation studies confirm that RIT offers more power than univariate differential expression analysis when multivariate effects are present. We apply RIT to gene expression data sets from diabetes and cancer studies, revealing several putative disease genes that were not detected by univariate differential expression analysis.
The proposed RIT algorithm increases the power of gene expression analysis by considering multivariate effects while retaining error rate control, and may be useful when conventional differential expression tests yield few findings.