Detecting multivariate differentially expressed genes

Background: 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. Results: 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 ...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér
• Keywords: BMCBI 2007, Expression Level, Expression Patterns, Gene Expression