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mixOmics

This is thereleased version of mixOmics; for the devel version, seemixOmics.

Omics Data Integration Project

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DOI: 10.18129/B9.bioc.mixOmics


Bioconductor version: Release (3.22)

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], Max Bladen [ctb], Benoit Gautier [ctb], Francois Bartolo [ctb], Pierre Monget [ctb], Jeff Coquery [ctb], FangZou Yao [ctb], Benoit Liquet [ctb], Eva Hamrud [ctb, cre]

Maintainer: Eva Hamrud <mixomicsdeveloper at gmail.com>

Citation (from within R, entercitation("mixOmics")):

Installation

To install this package, start R (version "4.5") and enter:

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

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

Documentation

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

browseVignettes("mixOmics")
mixOmicsHTMLR Script
Reference ManualPDF
NEWSText

Need some help? Ask on the Bioconductor Support site!

Details

biocViewsClassification,GenePrediction,ImmunoOncology,Metabolomics,Metagenomics,Microarray,MultipleComparison,Proteomics,Regression,Sequencing,Software
Version6.34.0
In Bioconductor sinceBioC 3.8 (R-3.5) (7 years)
LicenseGPL (>= 2)
DependsR (>= 4.4.0),MASS,lattice,ggplot2
Importsigraph,ellipse,corpcor,RColorBrewer, parallel,dplyr,tidyr,reshape2, methods,matrixStats,rARPACK,gridExtra, grDevices, graphics, stats,ggrepel,BiocParallel, utils,gsignal,rgl
System Requirements
URLhttp://www.mixOmics.org
Bug Reportshttps://github.com/mixOmicsTeam/mixOmics/issues/
See More
SuggestsBiocStyle,knitr,rmarkdown,mime,testthat,microbenchmark,magick,vdiffr,kableExtra,devtools
Linking To
Enhances
Depends On MetimeOmics,mixKernel,sgPLS
Imports MeAlpsNMR,DepecheR,PLSDAbatch,POMA,RFLOMICS,Coxmos,CytoProfile,Holomics,iTensor,plsmod,plsRcox,SISIR
Suggests Meautonomics,notameStats,planet,eoPredData,fastml,MetabolomicsBasics,MSclassifR,pctax,RVAideMemoire,SelectBoost,sharp
Links To Me
Build ReportBuild Report

Package Archives

FollowInstallation instructions to use this package in your R session.

Source PackagemixOmics_6.34.0.tar.gz
Windows Binary (x86_64) mixOmics_6.34.0.zip
macOS Binary (x86_64)mixOmics_6.34.0.tgz
macOS Binary (arm64)mixOmics_6.34.0.tgz
Source Repositorygit clone https://git.bioconductor.org/packages/mixOmics
Source Repository (Developer Access)git clone git@git.bioconductor.org:packages/mixOmics
Bioc Package Browserhttps://code.bioconductor.org/browse/mixOmics/
Package Short Urlhttps://bioconductor.org/packages/mixOmics/
Package Downloads ReportDownload Stats

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