Learning differential module networks across multiple experimental conditions

TL;DR

This paper reviews module network inference for gene regulatory networks, introduces protocols using Lemon-Tree software, and demonstrates learning differential networks across conditions with human gene expression data.

Contribution

It presents a comprehensive review and practical protocols for differential module network inference using Lemon-Tree, expanding applications to multiple experimental conditions.

Findings

Demonstrates application of Lemon-Tree to human gene expression data

Provides protocols for differential network inference

Shows effectiveness in reconstructing gene regulatory modules

Abstract

Module network inference is a statistical method to reconstruct gene regulatory networks, which uses probabilistic graphical models to learn modules of coregulated genes and their upstream regulatory programs from genome-wide gene expression and other omics data. Here we review the basic theory of module network inference, present protocols for common gene regulatory network reconstruction scenarios based on the Lemon-Tree software, and show, using human gene expression data, how the software can also be applied to learn differential module networks across multiple experimental conditions.