PCAtools

DOI: 10.18129/B9.bioc.PCAtools    

PCAtools: Everything Principal Components Analysis

Bioconductor version: Release (3.11)

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.

Author: Kevin Blighe [aut, cre], Anna-Leigh Brown [ctb], Myles Lewis [ctb], Aaron Lun [aut, ctb]

Maintainer: Kevin Blighe <kevin at clinicalbioinformatics.co.uk>

Citation (from within R, enter citation("PCAtools")):

Installation

To install this package, start R (version "4.0") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("PCAtools")

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

Documentation

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

browseVignettes("PCAtools")

 

HTML R Script PCAtools: everything Principal Component Analysis
PDF   Reference Manual
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Details

biocViews GeneExpression, RNASeq, Software, Transcription
Version 2.0.0
In Bioconductor since BioC 3.9 (R-3.6) (1 year)
License GPL-3
Depends ggplot2, ggrepel, reshape2, lattice, grDevices, cowplot
Imports methods, stats, utils, Matrix, DelayedMatrixStats, DelayedArray, BiocSingular, BiocParallel, Rcpp, dqrng
LinkingTo Rcpp, beachmat, BH, dqrng
Suggests testthat, scran, BiocGenerics, knitr, Biobase, GEOquery, biomaRt, ggplotify, beachmat
SystemRequirements C++11
Enhances
URL https://github.com/kevinblighe/PCAtools
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package PCAtools_2.0.0.tar.gz
Windows Binary PCAtools_2.0.0.zip (32- & 64-bit)
macOS 10.13 (High Sierra) PCAtools_2.0.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/PCAtools
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/PCAtools
Package Short Url https://bioconductor.org/packages/PCAtools/
Package Downloads Report Download Stats
Old Source Packages for BioC 3.11 Source Archive

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