Detecting multivariate differentially expressed genes.

ABSTRACT:

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 size

SUBMITTER: Nilsson R 

PROVIDER: S-EPMC1885271 | biostudies-literature | 2007 May

REPOSITORIES: biostudies-literature