scPCA

DOI: 10.18129/B9.bioc.scPCA    

This package is for version 3.12 of Bioconductor; for the stable, up-to-date release version, see scPCA.

Sparse Contrastive Principal Component Analysis

Bioconductor version: 3.12

A toolbox for sparse contrastive principal component analysis (scPCA) of high-dimensional biological data. scPCA combines the stability and interpretability of sparse PCA with contrastive PCA's ability to disentangle biological signal from unwanted variation through the use of control data. Also implements and extends cPCA.

Author: Philippe Boileau [aut, cre, cph] , Nima Hejazi [aut] , Sandrine Dudoit [ctb, ths]

Maintainer: Philippe Boileau <philippe_boileau at berkeley.edu>

Installation

To install this package, start R (version "4.0") and enter:

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

BiocManager::install("scPCA")

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("scPCA")

 

HTML	R Script	Sparse contrastive principal component analysis
PDF		Reference Manual
Text		NEWS
Text		LICENSE

Details

biocViews	DifferentialExpression, GeneExpression, Microarray, PrincipalComponent, RNASeq, Sequencing, Software
Version	1.4.0
In Bioconductor since	BioC 3.10 (R-3.6) (1.5 years)
License	MIT + file LICENSE
Depends	R (>= 3.6)
Imports	stats, methods, assertthat, tibble, dplyr, purrr, stringr, Rdpack, matrixStats, BiocParallel, elasticnet, sparsepca, cluster, kernlab, origami, RSpectra, coop, Matrix, DelayedArray
LinkingTo
Suggests	DelayedMatrixStats, sparseMatrixStats, testthat (>= 2.1.0), covr, knitr, rmarkdown, BiocStyle, ggplot2, ggpubr, splatter, SingleCellExperiment, microbenchmark
SystemRequirements
Enhances
URL	https://github.com/PhilBoileau/scPCA
BugReports	https://github.com/PhilBoileau/scPCA/issues
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	scPCA_1.4.0.tar.gz
Windows Binary	scPCA_1.4.0.zip
macOS 10.13 (High Sierra)	scPCA_1.4.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/scPCA
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/scPCA
Package Short Url	https://bioconductor.org/packages/scPCA/
Package Downloads Report	Download Stats