Dennis Kostka1, Rainer Spang. 1. Max Planck Institute for Molecular Genetics, Berlin, Germany. dennis.kostka@molgen.mpg.de
Abstract
MOTIVATION: Standard analysis routines for microarray data aim at differentially expressed genes. In this paper, we address the complementary problem of detecting sets of differentially co-expressed genes in two phenotypically distinct sets of expression profiles. RESULTS: We introduce a score for differential co-expression and suggest a computationally efficient algorithm for finding high scoring sets of genes. The use of our novel method is demonstrated in the context of simulations and on real expression data from a clinical study.
MOTIVATION: Standard analysis routines for microarray data aim at differentially expressed genes. In this paper, we address the complementary problem of detecting sets of differentially co-expressed genes in two phenotypically distinct sets of expression profiles. RESULTS: We introduce a score for differential co-expression and suggest a computationally efficient algorithm for finding high scoring sets of genes. The use of our novel method is demonstrated in the context of simulations and on real expression data from a clinical study.
Authors: Bai Zhang; Huai Li; Rebecca B Riggins; Ming Zhan; Jianhua Xuan; Zhen Zhang; Eric P Hoffman; Robert Clarke; Yue Wang Journal: Bioinformatics Date: 2008-12-26 Impact factor: 6.937
Authors: Bai Zhang; Ye Tian; Lu Jin; Huai Li; Ie-Ming Shih; Subha Madhavan; Robert Clarke; Eric P Hoffman; Jianhua Xuan; Leena Hilakivi-Clarke; Yue Wang Journal: Bioinformatics Date: 2011-02-03 Impact factor: 6.937