mixOmics: Omics Data Integration Project

We provide statistical integrative techniques and variants to analyse highly dimensional data sets: regularized Canonical Correlation Analysis ('rCCA') and sparse Partial Least Squares variants ('sPLS') to unravel relationships between two heterogeneous data sets of size (n times p) and (n times q) where the p and q variables are measured on the same samples or individuals n. These data may come from high throughput technologies, such as 'omics' data (e.g. transcriptomics, metabolomics or proteomics data) that require an integrative or joint analysis. However, 'mixOmics' can also be applied to any other large data sets where p + q >> n. 'rCCA' is a regularized version of Canonical Correlation Analysis to deal with the large number of variables. 'sPLS' allows variable selection in a one step procedure and two frameworks are proposed: regression and canonical analysis. Numerous graphical outputs are provided to help interpreting the results. Recent methodological developments include: sparse PLS-Discriminant Analysis ('sPLS-DA'), Independent Principal Component Analysis ('IPCA'), multilevel analysis using variance decomposition of the data and integration of multiple data sets with regularized Generalised Canonical Correlation Analysis ('rGCCA') and variants (sparse 'GCCA'). More details can be found on our website.

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