Package: PCAtools
Type: Package
Title: PCAtools: Everything Principal Components Analysis
Version: 2.22.4
Authors@R: c(
    person("Kevin", "Blighe", role = c("aut")),
    person("Jared", "Andrews", role = c("aut", "cre"), email = "jared.andrews07@gmail.com",
        comment = c(ORCID = "0000-0002-0780-6248")),
    person("Anna-Leigh", "Brown", role = c("ctb")),
    person("Vincent", "Carey", role = c("ctb")),
    person("Guido", "Hooiveld", role = c("ctb")),
    person("Aaron", "Lun", role = c("aut", "ctb")))
Description: 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.
License: GPL-3
Depends: ggplot2, ggrepel
Imports: lattice, grDevices, cowplot, methods, reshape2, stats, Matrix,
        DelayedMatrixStats, DelayedArray, beachmat (>= 2.21.1),
        BiocSingular, BiocParallel, Rcpp, dqrng
Suggests: testthat, scran, BiocGenerics, knitr, Biobase, GEOquery,
        hgu133a.db, ggplotify, RMTstat, ggforce, concaveman, DESeq2,
        airway, org.Hs.eg.db, magrittr, rmarkdown
LinkingTo: Rcpp, beachmat, assorthead, BH, dqrng
URL: https://github.com/kevinblighe/PCAtools
biocViews: RNASeq, ATACSeq, GeneExpression, Transcription, SingleCell,
        PrincipalComponent
VignetteBuilder: knitr
SystemRequirements: C++17
RoxygenNote: 7.3.3
Encoding: UTF-8
Config/pak/sysreqs: libicu-dev zlib1g-dev
Repository: https://bioc-release.r-universe.dev
Date/Publication: 2026-02-02 15:51:20 UTC
RemoteUrl: https://github.com/bioc/PCAtools
RemoteRef: RELEASE_3_22
RemoteSha: 97f269b88c8fa5954aacb152bfd3519e355b7383
NeedsCompilation: yes
Packaged: 2026-02-03 04:19:57 UTC; root
Author: Kevin Blighe [aut],
  Jared Andrews [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-0780-6248>),
  Anna-Leigh Brown [ctb],
  Vincent Carey [ctb],
  Guido Hooiveld [ctb],
  Aaron Lun [aut, ctb]
Maintainer: Jared Andrews <jared.andrews07@gmail.com>
Built: R 4.5.2; x86_64-w64-mingw32; 2026-02-03 04:22:48 UTC; windows
Archs: x64
