Package: integrOmics
Type: Package
Title: Integrate Omics data project
Version: 2.1
Date: 2009-06-26
Depends: igraph
Author: Sebastien Dejean, Ignacio Gonzalez and Kim-Anh Le Cao
Maintainer: Kim-Anh Le Cao <k.lecao@uq.edu.au>
Description: The package supplies two efficients nethodologies:
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, among which 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.
License: GPL (>= 2)
Packaged: 2009-06-25 22:50:07 UTC; k.lecao
Repository: CRAN
Date/Publication: 2009-06-26 16:51:52

Package: integrOmics
Type: Package
Title: Integrate Omics data project
Version: 2.3
Date: 2009-07-06
Depends: igraph
Author: Sebastien Dejean, Ignacio Gonzalez and Kim-Anh Le Cao
Maintainer: Kim-Anh Le Cao <k.lecao@uq.edu.au>
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,
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.
License: GPL (>= 2)
Packaged: 2009-07-07 06:04:14 UTC; k.lecao
Repository: CRAN
Date/Publication: 2009-07-18 16:11:34