scPCA

This is the development version of scPCA; for the stable release version, see scPCA.

All versions

3.24 (devel), 3.23 (release), 3.22, 3.21, 3.20, 3.19, 3.18, 3.17, 3.16, 3.15, 3.14, 3.13, 3.12, 3.11, 3.10

Sparse Contrastive Principal Component Analysis

Bioconductor version: Development (3.24)

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] ORCID: 0000-0002-4850-2507 , Nima Hejazi [aut] ORCID: 0000-0002-7127-2789 , Sandrine Dudoit [ctb, ths] ORCID: 0000-0002-6069-8629

Maintainer: Philippe Boileau <philippe_boileau at berkeley.edu>

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

Philippe Boileau, Nima Hejazi. scPCA: Sparse Contrastive Principal Component Analysis. doi:10.18129/B9.bioc.scPCA, R package version 1.27.0, https://bioconductor.org/packages/scPCA.

Generated from the package metadata; it may differ from the package's own citation.

Installation

To install this package, start R (version "4.6") and enter:

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

## The following initializes the development version of Bioconductor
BiocManager::install(version = "devel")

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

Need some help? Ask on the Bioconductor Support site!

Details

biocViews	DifferentialExpression, GeneExpression, Microarray, PrincipalComponent, RNASeq, Sequencing, Software
Version	1.27.0
In Bioconductor since	BioC 3.10 (R-3.6) (7 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

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, r-universe

Package Archives

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

Source Package	scPCA_1.27.0.tar.gz
Windows Binary (x86_64)	scPCA_1.27.0.zip
macOS Binary (big-sur-x86_64)	scPCA_1.27.0.tgz
macOS Binary (sonoma-arm64)	scPCA_1.27.0.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