Package: mixOmics
Type: Package
Title: Omics Data Integration Project
Version: 5.0-3
Date: 2012-12-11
Depends: R (>= 2.10), MASS, lattice
Imports: RGCCA, igraph, rgl, pheatmap
Author: Sebastien Dejean, Ignacio Gonzalez, Kim-Anh Le Cao with
contributions from Pierre Monget, Jeff Coquery, FangZou Yao,
Benoit Liquet, Florian Rohart
Maintainer: Kim-Anh Le Cao <k.lecao@uq.edu.au>
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.
License: GPL (>= 2)
Repository: CRAN
Date/Publication: 2014-09-01 15:47:59
Packaged: 2014-09-01 11:59:23 UTC; klecao
NeedsCompilation: no

Imports: igraph, rgl, ellipse, corpcor, RColorBrewer, plyr,parallel,
Author: Kim-Anh Le Cao, Florian Rohart, Ignacio Gonzalez, Sebastien Dejean with key contributors Benoit Gautier, Francois Bartolo
Description: Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: horizontal integration with regularised Generalised Canonical Correlation Analysis and vertical integration with multi-group Partial Least Squares.