recipe bioconductor-mixomics

Omics Data Integration Project

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/mixOmics.html

License:

GPL (>= 2)

Recipe:

/bioconductor-mixomics/meta.yaml

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.

package bioconductor-mixomics

versions:

6.30.0-0,  6.26.0-0,  6.24.0-0,  6.22.0-0,  6.17.26-0,  6.16.0-0,  6.14.0-1,  6.14.0-0,  6.12.0-0, 

6.30.0-0,  6.26.0-0,  6.24.0-0,  6.22.0-0,  6.17.26-0,  6.16.0-0,  6.14.0-1,  6.14.0-0,  6.12.0-0,  6.10.1-0,  6.8.0-1,  6.8.0-0,  6.6.2-0,  6.6.0-0

depends bioconductor-biocparallel:

>=1.40.0,<1.41.0

depends r-base:

>=4.4,<4.5.0a0

depends r-corpcor:

depends r-dplyr:

depends r-ellipse:

depends r-ggplot2:

depends r-ggrepel:

depends r-gridextra:

depends r-gsignal:

depends r-igraph:

depends r-lattice:

depends r-mass:

depends r-matrixstats:

depends r-rarpack:

depends r-rcolorbrewer:

depends r-reshape2:

depends r-rgl:

depends r-tidyr:

requirements:

additional platforms:

Installation

You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-mixomics

and update with::

   mamba update bioconductor-mixomics

To create a new environment, run:

mamba create --name myenvname bioconductor-mixomics

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull quay.io/biocontainers/bioconductor-mixomics:<tag>

(see `bioconductor-mixomics/tags`_ for valid values for ``<tag>``)

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