PCAtools

DOI: 10.18129/B9.bioc.PCAtools    

This package is for version 3.9 of Bioconductor; for the stable, up-to-date release version, see PCAtools.

PCAtools: everything Principal Components Analysis

Bioconductor version: 3.9

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.

Author: Kevin Blighe, Myles Lewis

Maintainer: Kevin Blighe <kevin at clinicalbioinformatics.co.uk>, Myles Lewis <myles.lewis at qmul.ac.uk>

Installation

To install this package, start R (version "3.6") 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 Components Analysis
PDF		Reference Manual
Text		NEWS

Details

biocViews	GeneExpression, RNASeq, Software, Transcription
Version	1.0.0
In Bioconductor since	BioC 3.9 (R-3.6) (< 6 months)
License	GPL-3
Depends	stats, ggplot2, ggrepel, reshape2, lattice, grDevices, cowplot
Imports
LinkingTo
Suggests	RUnit, BiocGenerics, knitr, Biobase, GEOquery, biomaRt, ggplotify
SystemRequirements
Enhances
URL	https://github.com/kevinblighe/PCAtools
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report

Package Archives

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

Source Package	PCAtools_1.0.0.tar.gz
Windows Binary	PCAtools_1.0.0.zip
Mac OS X 10.11 (El Capitan)	PCAtools_1.0.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/PCAtools
Source Repository (Developer Access)	git clone [email protected]:packages/PCAtools
Package Short Url	https://bioconductor.org/packages/PCAtools/
Package Downloads Report	Download Stats