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

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 compared to state-of-the-art biclustering methods. Further, we develop a principled method to recover context specific gene co-expression networks from the estimated sparse biclustering matrices. We apply BicMix to breast cancer gene expression data and to gene expression data from a cardiovascular study cohort, and we recover gene co-expression networks that are differential across ER+ and ER- samples and across male and female samples. We apply BicMix to the Genotype-Tissue Expression (GTEx) pilot data, and we find tissue specific gene networks. We validate these findings by using our tissue specific networks to identify trans-eQTLs specific to one of four primary tissues.Author Summary: Recovering gene co-expression networks from high-throughput experiments to measure gene expression levels is essential for understanding the genetic regulation of complex traits. It is often assumed for simplicity that gene co-expression networks are static across different contexts—e.g., drug exposure, genotype, tissue, age, and sex. The biological reality is that, along with differences in gene expression levels, there are differences in gene interactions across contexts. In this work, we describe a model for Bayesian biclustering, or recovering non-disjoint clusters of co-expressed genes in subsets of samples using gene expression level data. Using results from our biclustering model, we build gene co-expression networks jointly across all genes by computing the full regularized covariance matrix between all pairs of genes instead of testing each possible edge separately. Because biclustering recovers structure in subsets of the samples, we are able to recover gene co-expression networks that occur across all samples, that are differential across contexts (e.g., up-regulated in males and down-regulated in females), and that are unique to a context (e.g., only co-expressed in lung tissue). We illustrate the robustness of our approach and biologically validate the networks recovered from three different gene expression data sets.