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              <p>Motivation: The association between two blocks of ‘omics’ data brings challenging issues in computationalbiology due to their size and complexity. Here, we focus on a class of multivariate statisticalmethods called partial least square (PLS). Sparse version of PLS (sPLS) operates integration oftwo datasets while simultaneously selecting the contributing variables. However, these methodsdo not take into account the important structural or group effects due to the relationship betweenmarkers 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 algorithmenables to study the relationship between two different types of omics data (e.g. SNP andgene 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 thecontext of grouped data. Then, these methods are compared through an HIV therapeutic vaccinetrial. Our approaches provide parsimonious models to reveal the relationship between gene abundanceand the immunological response to the vaccine.</p>
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