Package: mixOmics
Type: Package
Title: Omics Data Integration Project
Version: 6.35.0
Depends: R (>= 4.4.0), MASS, lattice, ggplot2
Imports: igraph, ellipse, corpcor, RColorBrewer, parallel, dplyr,
        tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra,
        grDevices, graphics, stats, ggrepel, BiocParallel, utils,
        gsignal, rgl
Suggests: BiocStyle, knitr, rmarkdown, mime, testthat, microbenchmark,
        magick, vdiffr, kableExtra, devtools
Authors@R: 
    c(person("Kim-Anh", "Le Cao", role = "aut", email = "kimanh.lecao@unimelb.edu.au"), 
      person("Florian", "Rohart", role = "aut"), 
      person("Ignacio", "Gonzalez", role = "aut"),
      person("Sebastien", "Dejean", role = "aut"), 
      ## key contributors
      person("Al J", "Abadi", role = "ctb", email = "al.jal.abadi@gmail.com"), 
      person("Max", "Bladen", role = "ctb", email = "mbladen19@gmail.com"), 
      person("Benoit", "Gautier", role = "ctb"), 
      person("Francois", "Bartolo", role = "ctb"), 
      ## also contributions from
      person("Pierre", "Monget", role = "ctb"),
      person("Jeff", "Coquery", role = "ctb"),
      person("FangZou", "Yao", role = "ctb"),
      person("Benoit", "Liquet", role = "ctb"),
      person("Eva", "Hamrud", role = c("ctb", "cre"), email = "mixomicsdeveloper@gmail.com"))
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: http://www.mixOmics.org
BugReports: https://github.com/mixOmicsTeam/mixOmics/issues/
VignetteBuilder: knitr
Date: 2021-07-15
Packaged: 2025-11-02 04:05:21 UTC; root
NeedsCompilation: no
biocViews: ImmunoOncology, Microarray, Sequencing, Metabolomics,
        Metagenomics, Proteomics, GenePrediction, MultipleComparison,
        Classification, Regression
RoxygenNote: 7.3.2
Encoding: UTF-8
Config/pak/sysreqs: libfreetype6-dev libglpk-dev libglu1-mesa-dev make
        libicu-dev libpng-dev libxml2-dev libgl1-mesa-dev zlib1g-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2025-10-29 14:48:48 UTC
RemoteUrl: https://github.com/bioc/mixOmics
RemoteRef: HEAD
RemoteSha: 6f91947288f5e6ec9eead6792df20a5abaff18db
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@gmail.com>
Built: R 4.6.0; ; 2025-11-02 04:08:41 UTC; windows
