Omics Data Integration Project
Description
The package provide statistical integrative techniques and
variants to analyse highly dimensional data sets: 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. Recent
methodological developments include: sparse PLS-Discriminant
Analysis, Independent Principal Component Analysis and
multilevel analysis using variance decomposition of the data.