Package: PCAtools
Type: Package
Title: PCAtools: Everything Principal Components Analysis
Version: 1.2.0
Authors@R: c(
    person("Kevin", "Blighe", role=c("aut", "cre"), email="kevin@clinicalbioinformatics.co.uk"),
    person("Myles", "Lewis", role=c("ctb")),
    person("Aaron", "Lun", role="ctb"))
Description: Principal Components 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, whilst at the same time being capable of easy interpretation on the original data.
License: GPL-3
Depends: ggplot2, ggrepel, reshape2, lattice, grDevices, cowplot
Imports: methods, stats, utils, Matrix, DelayedMatrixStats,
        DelayedArray, BiocSingular, BiocParallel, Rcpp, dqrng
Suggests: testthat, scran, BiocGenerics, knitr, Biobase, GEOquery,
        biomaRt, ggplotify, beachmat
LinkingTo: Rcpp, beachmat, BH, dqrng
URL: https://github.com/kevinblighe/PCAtools
biocViews: RNASeq, GeneExpression, Transcription
VignetteBuilder: knitr
SystemRequirements: C++11
RoxygenNote: 6.1.1
git_url: https://git.bioconductor.org/packages/PCAtools
git_branch: RELEASE_3_10
git_last_commit: 638f4c1
git_last_commit_date: 2019-10-29
Date/Publication: 2019-10-29
NeedsCompilation: yes
Packaged: 2019-10-30 04:49:17 UTC; biocbuild
Author: Kevin Blighe [aut, cre],
  Myles Lewis [ctb],
  Aaron Lun [ctb]
Maintainer: Kevin Blighe <kevin@clinicalbioinformatics.co.uk>
Built: R 3.6.1; i386-w64-mingw32; 2019-10-30 13:52:51 UTC; windows
Archs: i386, x64
