Multivariate dependence and genetic networks inference

TL;DR

This paper introduces a new method based on maximum entropy to identify complex multivariate gene interactions, revealing higher-order dependencies in biological networks that traditional pairwise methods miss.

Contribution

It defines multivariate dependence using maximum entropy and develops computational tests to detect higher-order gene interactions in biological data.

Findings

Third-order statistics reveal gene interactions missed by second-order analysis.

Method uncovers dependencies in undersampled datasets.

Application to human B cells shows cooperative gene regulation pathways.

Abstract

A critical task in systems biology is the identification of genes that interact to control cellular processes by transcriptional activation of a set of target genes. Many methods have been developed to use statistical correlations in high-throughput datasets to infer such interactions. However, cellular pathways are highly cooperative, often requiring the joint effect of many molecules, and few methods have been proposed to explicitly identify such higher-order interactions, partially due to the fact that the notion of multivariate statistical dependency itself remains imprecisely defined. We define the concept of dependence among multiple variables using maximum entropy techniques and introduce computational tests for their identification. Synthetic network results reveal that this procedure uncovers dependencies even in undersampled regimes, when the joint probability distribution cannot be reliably estimated. Analysis of microarray data from human B cells reveals that third-order statistics, but not second-order ones, uncover relationships between genes that interact in a pathway to cooperatively regulate a common set of targets.