To install this package, start R and enter:
## try http:// if https:// URLs are not supported source("https://bioconductor.org/biocLite.R") biocLite("pcaMethods")
In most cases, you don't need to download the package archive at all.
This package is for version 3.4 of Bioconductor; for the stable, up-to-date release version, see pcaMethods.
Bioconductor version: 3.4
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")
):
To install this package, start R and enter:
## try http:// if https:// URLs are not supported source("https://bioconductor.org/biocLite.R") biocLite("pcaMethods")
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("pcaMethods")
R Script | Data with outliers | |
R Script | Introduction | |
R Script | Missing value imputation | |
Reference Manual |
biocViews | Bayesian, Software |
Version | 1.66.0 |
In Bioconductor since | BioC 1.9 (R-2.4) (10.5 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 | CompGO, DAPAR, MSnbase, PanVizGenerator, scde, SomaticSignatures |
Suggests Me | mtbls2 |
Build Report |
Follow Installation instructions to use this package in your R session.
Package Source | pcaMethods_1.66.0.tar.gz |
Windows Binary | pcaMethods_1.66.0.zip (32- & 64-bit) |
Mac OS X 10.9 (Mavericks) | pcaMethods_1.66.0.tgz |
Subversion source | (username/password: readonly) |
Git source | https://github.com/Bioconductor-mirror/pcaMethods/tree/release-3.4 |
Package Short Url | http://bioconductor.org/packages/pcaMethods/ |
Package Downloads Report | Download Stats |
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