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PCAtools: Everything Principal Components Analysis

Bioconductor version: Release (3.19)

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>

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


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


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


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

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


biocViews ATACSeq, GeneExpression, PrincipalComponent, RNASeq, SingleCell, Software, Transcription
Version 2.16.0
In Bioconductor since BioC 3.9 (R-3.6) (5 years)
License GPL-3
Depends ggplot2, ggrepel
Imports lattice, grDevices, cowplot, methods, reshape2, stats, Matrix, DelayedMatrixStats, DelayedArray, BiocSingular, BiocParallel, Rcpp, dqrng
System Requirements C++11
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Suggests testthat, scran, BiocGenerics, knitr, Biobase, GEOquery, hgu133a.db, ggplotify, beachmat, RMTstat, ggalt, DESeq2, airway,, magrittr, rmarkdown
Linking To Rcpp, beachmat, BH, dqrng
Depends On Me OSCA.advanced
Imports Me COTAN, CRISPRball, regionalpcs
Suggests Me RNAseqCovarImpute, scDataviz
Links To Me
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Source Package PCAtools_2.16.0.tar.gz
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macOS Binary (x86_64) PCAtools_2.16.0.tgz
macOS Binary (arm64) PCAtools_2.16.0.tgz
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