mixOmics

Omics Data Integration Project

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 variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares.

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Description file content

Package
mixOmics
Type
Package
Title
Omics Data Integration Project
Version
6.3.2
Date
2018-06-01
Depends
R (>= 2.10), MASS, lattice, ggplot2
Imports
igraph, rgl, ellipse, corpcor, RColorBrewer, parallel, dplyr, tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra
Author
Kim-Anh Le Cao, Florian Rohart, Ignacio Gonzalez, Sebastien Dejean with key contributors Benoit Gautier, Francois Bartolo and contributions from Pierre Monget, Jeff Coquery, FangZou Yao, Benoit Liquet.
Maintainer
Kim-Anh Le Cao
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 variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares.
License
GPL (>= 2)
URL
BugReports
https://bitbucket.org/klecao/package-mixomics/issues
Repository
CRAN
Date/Publication
2018-06-01 14:55:43 UTC
Packaged
2018-06-01 13:47:56 UTC; kim-anh
NeedsCompilation
no
LazyData
true

install.packages('mixOmics')

6.3.2

a year ago

http://www.mixOmics.org

Kim-Anh Le Cao

GPL (>= 2)

Depends on

R (>= 2.10), MASS, lattice, ggplot2

Imports

igraph, rgl, ellipse, corpcor, RColorBrewer, parallel, dplyr, tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra

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