Package: PCAtools
Type: Package
Title: PCAtools: Everything Principal Components Analysis
Version: 2.14.0
Authors@R: c(
    person("Kevin", "Blighe", role = c("aut", "cre"), email = "kevin@clinicalbioinformatics.co.uk"),
    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, BiocSingular, BiocParallel,
        Rcpp, dqrng
Suggests: testthat, scran, BiocGenerics, knitr, Biobase, GEOquery,
        hgu133a.db, ggplotify, beachmat, RMTstat, ggalt, DESeq2,
        airway, org.Hs.eg.db, magrittr, rmarkdown
LinkingTo: Rcpp, beachmat, BH, dqrng
URL: https://github.com/kevinblighe/PCAtools
biocViews: RNASeq, ATACSeq, GeneExpression, Transcription, SingleCell,
        PrincipalComponent
VignetteBuilder: knitr
SystemRequirements: C++11
RoxygenNote: 7.1.1
Encoding: UTF-8
git_url: https://git.bioconductor.org/packages/PCAtools
git_branch: RELEASE_3_18
git_last_commit: 42f92cf
git_last_commit_date: 2023-10-24
Date/Publication: 2023-11-06
NeedsCompilation: yes
Packaged: 2023-11-07 09:19:46 UTC; biocbuild
Author: Kevin Blighe [aut, cre],
  Anna-Leigh Brown [ctb],
  Vincent Carey [ctb],
  Guido Hooiveld [ctb],
  Aaron Lun [aut, ctb]
Maintainer: Kevin Blighe <kevin@clinicalbioinformatics.co.uk>
Built: R 4.3.2; aarch64-apple-darwin20; 2023-11-08 18:07:50 UTC; unix
