Learning differential module networks across multiple experimental conditions

Pau Erola, Eric Bonnet, Tom Michoel

Research output: Working paper

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
PublisherArXiv
Publication statusPublished - 24 Nov 2017

Keywords

  • q-bio.QM
  • q-bio.GN
  • q-bio.MN

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