Erola, Pau; Bonnet, Eric; Michoel, Tom
Methods in molecular biology (Clifton, N.J.),
2019, Letnik:
1883
Journal Article, Book Chapter
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.
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