Network-based mixture models for genomic data.

Abstract

A common task in genomic studies is to identify genes satisfying certain conditions, such as differentially expressed genes between normal and tumor tissues or regulatory target genes of a transcription factor (TF). Standard approaches treat all the genes identically and independently a priori and ignore the fact that genes work coordinately in biological processes as dictated by gene networks, leading to inefficient analysis and reduced power. We propose incorporating gene network information as prior biological knowledge into statistical modeling of genomic data to maximize the power for biological discoveries. We propose a spatially correlated mixture model based on the use of latent Gaussian Markov random fields (GMRF) to smooth gene specific prior probabilities in a mixture model over a network, assuming that neighboring genes in a network are functionally more similar to each other. In addition, we propose a Bayesian implementation of a discrete Markov random field (DMRF)-based mixture model for incorporating gene network information, and compare its performance with that based on Gaussian Markov random fields. We also extend the network-based mixture models to ones that are able to integrate multiple gene networks and diverse types of genomic data, such as protein- DNA binding, gene expression and DNA sequence data, to accurately identify regulatory target genes of a TF. Applications to high-throughput microarray data, along with simulations, demonstrate the utility of the new methods and the statistical efficiency gains over other methods.