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Learning Differential Module Networks Across Multiple Experimental Conditions

  • Pau Erola
  • Eric Bonnet
  • Tom MichoelEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1883)

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.

Key words

Gene regulatory network inference Module networks Differential networks Bayesian analysis 

Notes

Acknowledgements

PE and TM are supported by Roslin Institute Strategic Programme funding from the BBSRC [BB/P013732/1].

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Division of Genetics and Genomics, Roslin InstituteUniversity of EdinburghMidlothianUK
  2. 2.Centre National de Recherche en Génomique Humaine, Institut de Biologie François Jacob, Direction de la Recherche FondamentaleCEAEvryFrance
  3. 3.Division of Genetics and Genomics, The Roslin InstituteUniversity of EdinburghMidlothianUK
  4. 4.Current Address: Computational Biology UnitDepartment of Informatics, University of BergenBergenNorway

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