Learning Differential Module Networks Across Multiple Experimental Conditions

Pau Erola, Eric Bonnet, Tom Michoel

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

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.

Original languageEnglish
Title of host publicationGene Regulatory Networks
Pages303-321
Number of pages19
Volume1883
DOIs
Publication statusPublished - 2019

Publication series

NameMethods in Molecular Biology
PublisherHumana Press
ISSN (Print)1064-3745

Keywords

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

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