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

Benoît Liquet | Zendy; Pierre Lafaye de Micheaux | Zendy; Boris P. Hejblum | Zendy; Rodolphe Thiébaut | Zendy

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

Author(s) -

Benoît Liquet,

Pierre Lafaye de Micheaux,

Boris P. Hejblum,

Rodolphe Thiébaut

Publication year - 2015

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btv535

Subject(s) - context (archaeology) , multivariate statistics , computational biology , relevance (law) , computer science , genomics , snp , multivariate analysis , omics , data mining , biology , bioinformatics , machine learning , gene , genome , genetics , single nucleotide polymorphism , genotype , paleontology , political science , law

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.

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