Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering.

ABSTRACT: Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes that covary in all of the samples or in only a subset of the samples. Our biclustering method, BicMix, allows overcomplete representations of the data, computational tractability, and joint modeling of unknown confounders and biological signals. Compared with related biclustering methods, BicMix recovers latent structure with higher precision across diverse simulation scenarios as com

SUBMITTER: Gao C 

PROVIDER: S-EPMC4965098 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature