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Poster
The R package FANet: sparse factor analysis model for high dimensional gene co-expression networks
Abstract : Inference on gene regulatory networks from high-throughput expression data turns out to be one of the
main current challenges in systems biology. Such interaction networks are very insightful for the deep
understanding of biological relationships between genes. In particular, a functional characterization of gene
modules of highly interacting genes enables the identification of biological processes underlying complex
traits as diseases. Inference on this dependence structure shall account for both the high dimension of the
data and the sparsity of the interaction network.
The R package FANet provides a powerful method for estimating high dimensional co-expression networks.
Extending the idea introduced for differential analysis by Blum et al. [1] and Friguet et al. [2] we suggest
to take advantage of a low-dimensional latent linear structure of dependence to improve the stability of
correlation estimations. We propose an EM algorithm to fit a sparse factor model for correlations and
demonstrate how it helps extracting modules of genes and more generally improves the gene clustering
performance. Two functions are available in FANet package in order to introduce sparsity in the network
estimation. One function is based on a LASSO estimation using a cyclic coordinate descent algorithm. As an
alternative, the second function is based on biological knowledge integration as Gene Ontology annotation.
Finally, FANet results can serve as an input for WGCNA (Langfelder and Horvath [3]) procedure for gene
modules detection.
Littérature citée [3 références]
https://hal.archives-ouvertes.fr/hal-01211038
Déposant : Archive Ouverte Prodinra <>
Soumis le : mercredi 3 juin 2020 - 06:04:08
Dernière modification le : lundi 6 juillet 2020 - 15:40:07
Archivage à long terme le : : jeudi 3 décembre 2020 - 02:13:08
Déposant : Archive Ouverte Prodinra <>
Soumis le : mercredi 3 juin 2020 - 06:04:08
Dernière modification le : lundi 6 juillet 2020 - 15:40:07
Archivage à long terme le : : jeudi 3 décembre 2020 - 02:13:08
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UserS_2014_blum_FANet_1.pdf
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