integrOmics (1.0)

Integrate Omics data project.

The package supplies two efficients nethodologies: regularized CCA and sparse PLS to unravel relationships between two heterogeneous data sets coming from high throughput technologies, such as omics data such as transcriptomics, metabolomics or proteomics data, but also other large data sets. Both methodologies allow to integrate two data sets when the two types of variables are measured on the same observations (samples). rCCA is a regularized version of CCA to deal with the large number of variables. sPLS allows variables selection in a one step procedure and two frameworks are proposed: regression and canonical analysis. 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: Does not use any package
Reverse depends: integrativeME

Released over 10 years ago.