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pcaMethods

A collection of PCA methods


Bioconductor version: Release (3.18)

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>

Citation (from within R, enter citation("pcaMethods")):

Installation

To install this package, start R (version "4.3") 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")
Data with outliers PDF R Script
Introduction PDF R Script
Missing value imputation PDF R Script
Reference Manual PDF

Details

biocViews Bayesian, Software
Version 1.94.0
In Bioconductor since BioC 1.9 (R-2.4) (17.5 years)
License GPL (>= 3)
Depends Biobase, methods
Imports BiocGenerics, Rcpp (>= 0.11.3), MASS
System Requirements Rcpp
URL https://github.com/hredestig/pcamethods
Bug Reports https://github.com/hredestig/pcamethods/issues
See More
Suggests matrixStats, lattice, ggplot2
Linking To Rcpp
Enhances
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 Build Report

Package Archives

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

Source Package pcaMethods_1.94.0.tar.gz
Windows Binary pcaMethods_1.94.0.zip (64-bit only)
macOS Binary (x86_64) pcaMethods_1.94.0.tgz
macOS Binary (arm64) pcaMethods_1.94.0.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