recipe bioconductor-pcatools

PCAtools: Everything Principal Components Analysis

Homepage:

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

License:

GPL-3

Recipe:

/bioconductor-pcatools/meta.yaml

Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data.

package bioconductor-pcatools

versions:

2.18.0-0,  2.14.0-0,  2.12.0-0,  2.10.0-1,  2.10.0-0,  2.6.0-2,  2.6.0-1,  2.6.0-0,  2.4.0-0, 

2.18.0-0,  2.14.0-0,  2.12.0-0,  2.10.0-1,  2.10.0-0,  2.6.0-2,  2.6.0-1,  2.6.0-0,  2.4.0-0,  2.2.0-1,  2.2.0-0,  2.0.0-0,  1.2.0-0,  1.0.0-1,  1.0.0-0

depends bioconductor-beachmat:

>=2.22.0,<2.23.0

depends bioconductor-beachmat:

>=2.22.0,<2.23.0a0

depends bioconductor-biocparallel:

>=1.40.0,<1.41.0

depends bioconductor-biocparallel:

>=1.40.0,<1.41.0a0

depends bioconductor-biocsingular:

>=1.22.0,<1.23.0

depends bioconductor-biocsingular:

>=1.22.0,<1.23.0a0

depends bioconductor-delayedarray:

>=0.32.0,<0.33.0

depends bioconductor-delayedarray:

>=0.32.0,<0.33.0a0

depends bioconductor-delayedmatrixstats:

>=1.28.0,<1.29.0

depends bioconductor-delayedmatrixstats:

>=1.28.0,<1.29.0a0

depends libblas:

>=3.9.0,<4.0a0

depends libcxx:

>=18

depends liblapack:

>=3.9.0,<4.0a0

depends r-base:

>=4.4,<4.5.0a0

depends r-bh:

depends r-cowplot:

depends r-dqrng:

depends r-ggplot2:

depends r-ggrepel:

depends r-lattice:

depends r-matrix:

depends r-rcpp:

depends r-reshape2:

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-pcatools

and update with::

   mamba update bioconductor-pcatools

To create a new environment, run:

mamba create --name myenvname bioconductor-pcatools

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-pcatools:<tag>

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

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