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Bioinformatics. 2016 Jan 1;32(1):35-42. doi: 10.1093/bioinformatics/btv535. Epub 2015 Sep 10.

Group and sparse group partial least square approaches applied in genomics context.

Author information

1
School of Mathematics and Physics, The University of Queensland, Brisbane 4066, Australia, ARC Centre of Excellence for Mathematical and Statistical Frontiers, QUT, Brisbane, Australia.
2
CREST, ENSAI, Campus de Ker-Lann, Rue Blaise Pascal, BP 37203, 35172 Bruz cedex, France.
3
Inria, SISTM, Talence and Inserm, U897, Bordeaux, Bordeaux University, Bordeaux and Vaccine Research Institute, Creteil, France.

Abstract

MOTIVATION:

The association between two blocks of 'omics' data brings challenging issues in computational biology due to their size and complexity. Here, we focus on a class of multivariate statistical methods called partial least square (PLS). Sparse version of PLS (sPLS) operates integration of two datasets while simultaneously selecting the contributing variables. However, these methods do not take into account the important structural or group effects due to the relationship between markers among biological pathways. Hence, considering the predefined groups of markers (e.g. genesets), this could improve the relevance and the efficacy of the PLS approach.

RESULTS:

We propose two PLS extensions called group PLS (gPLS) and sparse gPLS (sgPLS). Our algorithm enables to study the relationship between two different types of omics data (e.g. SNP and gene expression) or between an omics dataset and multivariate phenotypes (e.g. cytokine secretion). We demonstrate the good performance of gPLS and sgPLS compared with the sPLS in the context of grouped data. Then, these methods are compared through an HIV therapeutic vaccine trial. Our approaches provide parsimonious models to reveal the relationship between gene abundance and the immunological response to the vaccine.

AVAILABILITY AND IMPLEMENTATION:

The approach is implemented in a comprehensive R package called sgPLS available on the CRAN.

CONTACT:

b.liquet@uq.edu.au

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
26358727
DOI:
10.1093/bioinformatics/btv535
[Indexed for MEDLINE]

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