title: 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 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.
authors.affiliation: Department of Statistical Science, Duke University, Durham, North Carolina, United States of America.
authors.forename: Chuan
authors.identifier: 0000-0002-1399-8450
authors.initials: C
authors.lastname: Gao
authors.affiliation: Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, United States of America.
authors.forename: Ian C
authors.initials: IC
authors.lastname: McDowell
authors.affiliation: Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, United States of America.
authors.forename: Shiwen
authors.initials: S
authors.lastname: Zhao
authors.affiliation: Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Christopher D
authors.initials: CD
authors.lastname: Brown
authors.affiliation: Department of Computer Science, Center for Statistics and Machine Learning, Princeton University, Princeton, New Jersey, United States of America.
authors.forename: Barbara E
authors.initials: BE
authors.lastname: Engelhardt
mesh_terms: D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics*; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D008297:Male; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis