integrOmics (2.3)

Integrate Omics data project.

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, integrOmics 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.

Maintainer: Kim-Anh Le Cao
Author(s): Sebastien Dejean, Ignacio Gonzalez and Kim-Anh Le Cao

License: GPL (>= 2)

Uses: igraph
Reverse depends: integrativeME

Released over 10 years ago.