Sebastien Dejean, Ignacio Gonzalez, Kim-Anh Le Cao and Pierre
Monget

Sebastien Dejean, Ignacio Gonzalez, Kim-Anh Le Cao, Pierre
Monget and Jeff Coquery

R (>= 2.10), igraph, rgl, lattice, pheatmap
Sebastien Dejean, Ignacio Gonzalez, Kim-Anh Le Cao with
contributions from Pierre Monget, Jeff Coquery, FangZou Yao,
Benoit Liquet
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.

R (>= 2.10), igraph0, rgl, lattice, pheatmap

Orphaned on 2013-09-03 as it had not been converted to
igraph in over a year

Sebastien Dejean, Ignacio Gonzalez, Kim-Anh Le Cao with
contributions from Pierre Monget, Jeff Coquery, FangZou Yao,
Benoit Liquet, Florian Rohart

Kim-Anh Le Cao,  Ignacio Gonzalez, Sebastien Dejean with key contributors Florian Rohart, Benoit Gautier
and contributions from Pierre Monget, Jeff Coquery, FangZou Yao, Benoit Liquet.
We provide statistical integrative techniques and
variants to analyse highly dimensional data sets: regularized
Canonical Correlation Analysis ('rCCA') and sparse Partial Least Squares variants ('sPLS')
to unravel relationships between two heterogeneous data sets of size (n times p) and (n times q) 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 Canonical Correlation Analysis 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 ('sPLS-DA'), Independent
Principal Component Analysis ('IPCA'), multilevel analysis using variance decomposition of the data and integration
of multiple data sets with regularized Generalised Canonical Correlation Analysis ('rGCCA') and variants (sparse 'GCCA'). More details  can be found
on our website.