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scPCA

Sparse Contrastive Principal Component Analysis


Bioconductor version: Release (3.18)

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>

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

Installation

To install this package, start R (version "4.3") and enter:


if (!require("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")
Sparse contrastive principal component analysis HTML R Script
Reference Manual PDF
NEWS Text
LICENSE Text

Details

biocViews DifferentialExpression, GeneExpression, Microarray, PrincipalComponent, RNASeq, Sequencing, Software
Version 1.16.0
In Bioconductor since BioC 3.10 (R-3.6) (4.5 years)
License MIT + file LICENSE
Depends R (>= 4.0.0)
Imports stats, methods, assertthat, tibble, dplyr, purrr, stringr, Rdpack, matrixStats, BiocParallel, elasticnet, sparsepca, cluster, kernlab, origami, RSpectra, coop, Matrix, DelayedArray, ScaledMatrix, MatrixGenerics
System Requirements
URL https://github.com/PhilBoileau/scPCA
Bug Reports https://github.com/PhilBoileau/scPCA/issues
See More
Suggests DelayedMatrixStats, sparseMatrixStats, testthat (>= 2.1.0), covr, knitr, rmarkdown, BiocStyle, ggplot2, ggpubr, splatter, SingleCellExperiment, microbenchmark
Linking To
Enhances
Depends On Me OSCA.advanced, OSCA.workflows
Imports Me
Suggests Me
Links To Me
Build Report Build Report

Package Archives

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

Source Package scPCA_1.16.0.tar.gz
Windows Binary scPCA_1.16.0.zip
macOS Binary (x86_64) scPCA_1.16.0.tgz
macOS Binary (arm64) scPCA_1.16.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/scPCA
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/scPCA
Bioc Package Browser https://code.bioconductor.org/browse/scPCA/
Package Short Url https://bioconductor.org/packages/scPCA/
Package Downloads Report Download Stats