A semi-parametric Bayesian model for unsupervised differential co-expression analysis

Background: Differential co-expression analysis is an emerging strategy for characterizing disease related dysregulation of gene expression regulatory networks. Given pre-defined sets of biological samples, such analysis aims at identifying genes that are co-expressed in one, but not in the other set of samples. Results: We developed a novel probabilistic framework for jointly uncovering contexts (i.e. groups of samples) with specific co-expression patterns, and groups of genes with different co-expression patterns across such contexts. In contrast to current clustering and bi-clustering procedures, the implicit similarity measure in this model used for grouping biological samples is based on the clustering structure of genes within each sample and not on traditional measures of gene expression level similarities. Within this framework, biological samples with widely discordant expression patterns can be placed in the same context as long as the co-clustering structure of genes is con...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Johannes M. Freudenberg, Siva Sivaganesan, Michael Wagner, Mario Medvedovic
• Keywords: Biological Samples, BMCBI 2010, Business, Expression Regulatory Networks, Gene Expression