Package Recipe 'bioconductor-pcatools'

recipe bioconductor-pcatools

PCAtools: Everything Principal Components Analysis

Homepage:: https://bioconductor.org/packages/3.20/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:

2.22.4-0, 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,

2.22.4-0, 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:

on bioconductor-assorthead >=1.4.0,<1.5.0
on bioconductor-assorthead >=1.4.0,<1.5.0a0
on bioconductor-beachmat >=2.26.0,<2.27.0
on bioconductor-beachmat >=2.26.0,<2.27.0a0
on bioconductor-biocparallel >=1.44.0,<1.45.0
on bioconductor-biocparallel >=1.44.0,<1.45.0a0
on bioconductor-biocsingular >=1.26.0,<1.27.0
on bioconductor-biocsingular >=1.26.1,<1.27.0a0
on bioconductor-delayedarray >=0.36.0,<0.37.0
on bioconductor-delayedarray >=0.36.0,<0.37.0a0
on bioconductor-delayedmatrixstats >=1.32.0,<1.33.0
on bioconductor-delayedmatrixstats >=1.32.0,<1.33.0a0
on libblas >=3.9.0,<4.0a0
on libgcc >=14
on liblapack >=3.9.0,<4.0a0
on liblzma >=5.8.2,<6.0a0
on libstdcxx >=14
on libzlib >=1.3.1,<2.0a0
on r-base >=4.5,<4.6.0a0
on r-bh
on r-cowplot
on r-dqrng
on r-ggplot2
on r-ggrepel
on r-lattice
on r-matrix
on r-rcpp
on r-reshape2

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-pcatools

to add into an existing workspace instead, run:

pixi add bioconductor-pcatools

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-pcatools

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-pcatools

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

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

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

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-pcatools/README.html)