Learning Differential Module Networks Across Multiple Experimental Conditions

Methods Mol Biol. 2019:1883:303-321. doi: 10.1007/978-1-4939-8882-2_13.

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.

Keywords: Bayesian analysis; Differential networks; Gene regulatory network inference; Module networks.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Algorithms
  • Cluster Analysis
  • Computational Biology / instrumentation
  • Computational Biology / methods*
  • Datasets as Topic
  • Gene Expression Profiling / instrumentation
  • Gene Expression Profiling / methods
  • Gene Expression Regulation*
  • Gene Regulatory Networks*
  • Humans
  • Models, Genetic*
  • Software