PCAtools

DOI: 10.18129/B9.bioc.PCAtools    

PCAtools: Everything Principal Components Analysis

Bioconductor version: Release (3.13)

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], Vincent Carey [ctb], Guido Hooiveld [ctb], Aaron Lun [aut, ctb]

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

Installation

To install this package, start R (version "4.1") 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
Text		NEWS

Details

biocViews	ATACSeq, GeneExpression, PrincipalComponent, RNASeq, SingleCell, Software, Transcription
Version	2.4.0
In Bioconductor since	BioC 3.9 (R-3.6) (2.5 years)
License	GPL-3
Depends	ggplot2, ggrepel
Imports	lattice, grDevices, cowplot, methods, reshape2, stats, Matrix, DelayedMatrixStats, DelayedArray, BiocSingular, BiocParallel, Rcpp, dqrng
LinkingTo	Rcpp, beachmat, BH, dqrng
Suggests	testthat, scran, BiocGenerics, knitr, Biobase, GEOquery, hgu133a.db, ggplotify, beachmat, RMTstat, ggalt, DESeq2, airway, org.Hs.eg.db, magrittr, rmarkdown
SystemRequirements	C++11
Enhances
URL	https://github.com/kevinblighe/PCAtools
Depends On Me	OSCA.advanced
Imports Me
Suggests Me	scDataviz
Links To Me
Build Report

Package Archives

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

Source Package	PCAtools_2.4.0.tar.gz
Windows Binary	PCAtools_2.4.0.zip
macOS 10.13 (High Sierra)	PCAtools_2.4.0.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