Sparse factor model for co-expression networks with an application using prior biological knowledge
Houée-Bigot Magalie and
Causeur David ()
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Blum Yuna: Department of Medicine, David Geffen School of Medicine, A2-237 Center for Health Sciences, University of California, 10833 Le Conte Avenue, Los Angeles, CA 90095-1679, USA
Causeur David: Agrocampus Ouest, IRMAR, UMR 6625 CNRS, 65 rue de St-Brieuc CS84215, 35042 Rennes Cedex, France
Statistical Applications in Genetics and Molecular Biology, 2016, vol. 15, issue 3, 253-272
Inference on gene regulatory networks from high-throughput expression data turns out to be one of the main current challenges in systems biology. Such networks can be very insightful for the deep understanding of interactions between genes. Because genes-gene interactions is often viewed as joint contributions to known biological mechanisms, inference on the dependence among gene expressions is expected to be consistent to some extent with the functional characterization of genes which can be derived from ontologies (GO, KEGG, …). The present paper introduces a sparse factor model as a general framework either to account for a prior knowledge on joint contributions of modules of genes to latent biological processes or to infer on the corresponding co-expression network. We propose an ℓ1 – regularized EM algorithm to fit a sparse factor model for correlation. We demonstrate how it helps extracting modules of genes and more generally improves the gene clustering performance. The method is compared to alternative estimation procedures for sparse factor models of relevance networks in a simulation study. The integration of a biological knowledge based on the gene ontology (GO) is also illustrated on a liver expression data generated to understand adiposity variability in chicken.
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