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

TL;DR

This paper introduces a novel log-linear graphical model tailored for high-dimensional count data from high-throughput sequencing, enabling more accurate inference of gene networks than traditional Gaussian models.

Contribution

The authors develop a Poisson graphical model with a fast parallel algorithm, extending network inference to discrete sequencing data and demonstrating its effectiveness through simulations and a breast cancer microRNA case study.

Findings

Successfully recovers network structures from simulated count data.

Identifies known and novel microRNA regulators in breast cancer data.

Abstract

Gaussian graphical models are often used to infer gene networks based on microarray expression data. Many scientists, however, have begun using high-throughput sequencing technologies to measure gene expression. As the resulting high-dimensional count 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 a pair-wise Markov property: conditional on all other variables, each variable is Poisson. We estimate our model locally via neighborhood selection by fitting 1-norm penalized log-linear models. Additionally, we develop a fast parallel algorithm, an approach we call the Poisson Graphical Lasso, permitting us to fit our graphical model to high-dimensional genomic data sets. In simulations, we illustrate the effectiveness of our methods for recovering network structure from count data. A case study on breast cancer microRNAs, a novel application of graphical models, finds known regulators of breast cancer genes and discovers novel microRNA clusters and hubs that are targets for future research.