Gene selection heuristic algorithm for nutrigenomics studies.

Abstract:

:Large datasets from -omics studies need to be deeply investigated. The aim of this paper is to provide a new method (LEM method) for the search of transcriptome and metabolome connections. The heuristic algorithm here described extends the classical canonical correlation analysis (CCA) to a high number of variables (without regularization) and combines well-conditioning and fast-computing in "R." Reduced CCA models are summarized in PageRank matrices, the product of which gives a stochastic matrix that resumes the self-avoiding walk covered by the algorithm. Then, a homogeneous Markov process applied to this stochastic matrix converges the probabilities of interconnection between genes, providing a selection of disjointed subsets of genes. This is an alternative to regularized generalized CCA for the determination of blocks within the structure matrix. Each gene subset is thus linked to the whole metabolic or clinical dataset that represents the biological phenotype of interest. Moreover, this selection process reaches the aim of biologists who often need small sets of genes for further validation or extended phenotyping. The algorithm is shown to work efficiently on three published datasets, resulting in meaningfully broadened gene networks.

journal_name

Physiol Genomics

journal_title

Physiological genomics

authors

Valour D,Hue I,Grimard B,Valour B

doi

10.1152/physiolgenomics.00139.2012

subject

Has Abstract

pub_date

2013-07-15 00:00:00

pages

615-28

issue

14

eissn

1094-8341

issn

1531-2267

pii

physiolgenomics.00139.2012

journal_volume

45

pub_type

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