pcaMethods

DOI: 10.18129/B9.bioc.pcaMethods  

A collection of PCA methods

Bioconductor version: Release (3.16)

Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany.

Author: Wolfram Stacklies, Henning Redestig, Kevin Wright

Maintainer: Henning Redestig <henning.red at gmail.com>

Installation

To install this package, start R (version "4.2") and enter:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("pcaMethods")

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("pcaMethods")

 

PDF	R Script	Data with outliers
PDF	R Script	Introduction
PDF	R Script	Missing value imputation
PDF		Reference Manual

Details

biocViews	Bayesian, Software
Version	1.90.0
In Bioconductor since	BioC 1.9 (R-2.4) (16.5 years)
License	GPL (>= 3)
Depends	Biobase, methods
Imports	BiocGenerics, Rcpp (>= 0.11.3), MASS
LinkingTo	Rcpp
Suggests	matrixStats, lattice, ggplot2
SystemRequirements	Rcpp
Enhances
URL	https://github.com/hredestig/pcamethods
BugReports	https://github.com/hredestig/pcamethods/issues
Depends On Me	DeconRNASeq
Imports Me	autonomics, consensusDE, destiny, FRASER, MAI, MatrixQCvis, MSnbase, MSPrep, MultiBaC, OUTRIDER, PhosR, pmp, scde, SomaticSignatures
Suggests Me	cardelino, MsCoreUtils, mtbls2, QFeatures, qmtools
Links To Me
Build Report

Package Archives

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

Source Package	pcaMethods_1.90.0.tar.gz
Windows Binary	pcaMethods_1.90.0.zip
macOS Binary (x86_64)	pcaMethods_1.90.0.tgz
macOS Binary (arm64)	pcaMethods_1.90.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/pcaMethods
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/pcaMethods
Bioc Package Browser	https://code.bioconductor.org/browse/pcaMethods/
Package Short Url	https://bioconductor.org/packages/pcaMethods/
Package Downloads Report	Download Stats