mixOmics

DOI: 10.18129/B9.bioc.mixOmics  

Omics Data Integration Project

Bioconductor version: Release (3.16)

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.

Author: Kim-Anh Le Cao [aut], Florian Rohart [aut], Ignacio Gonzalez [aut], Sebastien Dejean [aut], Al J Abadi [ctb, cre], Benoit Gautier [ctb], Francois Bartolo [ctb], Pierre Monget [ctb], Jeff Coquery [ctb], FangZou Yao [ctb], Benoit Liquet [ctb]

Maintainer: Al J Abadi <al.jal.abadi at gmail.com>

Installation

To install this package, start R (version "4.2") and enter:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("mixOmics")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("mixOmics")

 

HTML	R Script	mixOmics
PDF		Reference Manual
Text		NEWS

Details

biocViews	Classification, GenePrediction, ImmunoOncology, Metabolomics, Metagenomics, Microarray, MultipleComparison, Proteomics, Regression, Sequencing, Software
Version	6.22.0
In Bioconductor since	BioC 3.8 (R-3.5) (4.5 years)
License	GPL (>= 2)
Depends	R (>= 3.5.0), MASS, lattice, ggplot2
Imports	igraph, ellipse, corpcor, RColorBrewer, parallel, dplyr, tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra, grDevices, graphics, stats, ggrepel, BiocParallel, utils
LinkingTo
Suggests	BiocStyle, knitr, rmarkdown, testthat, rgl
SystemRequirements
Enhances
URL	http://www.mixOmics.org
BugReports	https://github.com/mixOmicsTeam/mixOmics/issues/
Depends On Me	timeOmics
Imports Me	AlpsNMR, DepecheR, multiSight, POMA
Suggests Me	autonomics, netOmics
Links To Me
Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	mixOmics_6.22.0.tar.gz
Windows Binary	mixOmics_6.22.0.zip (64-bit only)
macOS Binary (x86_64)	mixOmics_6.22.0.tgz
macOS Binary (arm64)	mixOmics_6.22.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/mixOmics
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/mixOmics
Bioc Package Browser	https://code.bioconductor.org/browse/mixOmics/
Package Short Url	https://bioconductor.org/packages/mixOmics/
Package Downloads Report	Download Stats