Omics Data Integration Project
Description
The package supplies two efficients methodologies:
regularized CCA and sparse PLS to unravel relationships between
two heterogeneous data sets of size (nxp) and (nxq) 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 CCA 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.