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Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics$
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Christine Sinoquet and Raphaël Mourad

Print publication date: 2014

Print ISBN-13: 9780198709022

Published to Oxford Scholarship Online: December 2014

DOI: 10.1093/acprof:oso/9780198709022.001.0001

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Network Inference in Breast Cancer with Gaussian Graphical Models and Extensions

Network Inference in Breast Cancer with Gaussian Graphical Models and Extensions

Chapter:
(p.121) Chapter 5 Network Inference in Breast Cancer with Gaussian Graphical Models and Extensions
Source:
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics
Author(s):

Marine Jeanmougin

Camille Charbonnier

Mickaël Guedj

Julien Chiquet

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198709022.003.0005

Clustering genes with high correlations will group genes with close expression profiles, defining clusters of co-expressed genes. However, such correlations do not provide any clue on the chain of information going from gene to gene. Partial correlation consists in quantifying the correlation between two genes after excluding the effects of the other genes. Partial correlation thus makes it possible to distinguish between the correlation of two genes due to direct causal relationships from the correlation that originates via intermediate genes. In this chapter, Gaussian graphical model (GGM) learning is set up as a covariate selection problem. Two least absolute shrinkage and selection operator (LASSO)-type techniques are described, the graphical LASSO approach and the neighborhood selection. Then two extensions to the classical GGM are presented. GGMs are extended in structured GGMs, to account for modularity, and more generally heterogeneity in the gene connection features. The extension using a biological prior on the network structure is illustrated on real data.

Keywords:   gene network, Gaussian graphical models, LASSO

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