PCAtools
This is the development version of PCAtools; for the stable release version, see PCAtools.
PCAtools: Everything Principal Components Analysis
Bioconductor version: Development (3.18)
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 clinicalbioinformatics.co.uk>
citation("PCAtools")):
Installation
To install this package, start R (version "4.3") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
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")| PCAtools: everything Principal Component Analysis | HTML | R Script |
| Reference Manual | ||
| NEWS | Text |
Details
| biocViews | ATACSeq, GeneExpression, PrincipalComponent, RNASeq, SingleCell, Software, Transcription |
| Version | 2.13.0 |
| In Bioconductor since | BioC 3.9 (R-3.6) (4.5 years) |
| License | GPL-3 |
| Depends | ggplot2, ggrepel |
| Imports | lattice, grDevices, cowplot, methods, reshape2, stats, Matrix, DelayedMatrixStats, DelayedArray, BiocSingular, BiocParallel, Rcpp, dqrng |
| Linking To | Rcpp, beachmat, BH, dqrng |
| Suggests | testthat, scran, BiocGenerics, knitr, Biobase, GEOquery, hgu133a.db, ggplotify, beachmat, RMTstat, ggalt, DESeq2, airway, org.Hs.eg.db, magrittr, rmarkdown |
| System Requirements | C++11 |
| Enhances | |
| URL | https://github.com/kevinblighe/PCAtools |
See More
| Depends On Me | OSCA.advanced |
| Imports Me | |
| Suggests Me | scDataviz |
| Links To Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | PCAtools_2.13.0.tar.gz |
| Windows Binary | PCAtools_2.13.0.zip |
| macOS Binary (x86_64) | PCAtools_2.13.0.tgz |
| macOS Binary (arm64) | |
| Source Repository | git clone https://git.bioconductor.org/packages/PCAtools |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/PCAtools |
| Bioc Package Browser | https://code.bioconductor.org/browse/PCAtools/ |
| Package Short Url | https://bioconductor.org/packages/PCAtools/ |
| Package Downloads Report | Download Stats |