A Log-Linear Graphical Model for Inferring Genetic Networks from High-Throughput Sequencing Data

Details A Log-Linear Graphical Model for Inferring Genetic Networks from High-Throughput Sequencing Data A Log-Linear Graphical Model for Inferring Genetic Networks from High-Throughput Sequencing Data

2012-04-17

arxiv.org/abs/1204.3941v1

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Gaussian graphical models are often used to infer gene networks based on microarray expression data. Currently, however, many scientists are using high-throughput sequencing technologies to measure gene expression levels of all genes for a given sample. As the resulting high-dimensional data consists of counts of sequencing reads for each gene, Gaussian graphical models are not optimal for modeling gene networks based on this discrete data. We develop a novel method for estimating high-dimensional Poisson graphical models, the Log-Linear Graphical Model, allowing us to infer networks based on high-throughput sequencing data. Our model assumes that conditional on all other genes, each gene is Poisson, jointly defining a pair-wise Poisson Markov random field. We estimate our genetic networks via neighborhood selection by fitting `1-norm penalized log-linear models, an approach we call the Poisson Graphical Lasso. Additionally, we develop a fast parallel algorithm, permitting us to fit our graphical models to high-dimensional genomic data sets. In simulations and a novel application of Markov Networks to microRNA sequencing data, we illustrate the effectiveness of our methods for recovering genetic networks. Our estimated microRNA networks find known regulators of breast cancer genes and discover novel microRNA clusters and hubs that are targets for future research.

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