Strategies to analyse 'omics' data: From standard T-tests in genomics to a more general Bayesian approach

Recent technological advances in molecular biology have given rise to numerous large scale datasets whose analysis have risen serious methodological challenges mainly relating to the size and complex structure of the data. Considerable experience has been gained over the past decade, mainly in genetics, from the Genome-Wide Association Study (GWAS) era, and more recently in transcriptomics and metabolomics. Building upon the corresponding wide literature, we present methods used to analyze OMICS data within each of the three main types of approaches : univariate models, dimension reduction techniques, and variable selection models. We focus on methods for which available ready-to-use packages are available. In this context, we propose R2GUESS an R package which interface a C++ implementation of a fully sparse Bayesian variable selection (BVS) approach for linear regression that can analyze single and multiple responses in a integrated way. A simulation and an illustration in the context of GWAS is presented and show the performance of the BVS approach.