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scPCA

Sparse Contrastive Principal Component Analysis

Bioconductor version: 3.24 · Package version: 1.27.0

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.

Installation

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

BiocManager::install("scPCA")

Details

MaintainerPhilippe Boileau <philippe_boileau@berkeley.edu>
AuthorPhilippe Boileau [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-4850-2507>), Nima Hejazi [aut] (ORCID: <https://orcid.org/0000-0002-7127-2789>), Sandrine Dudoit [ctb, ths] (ORCID: <https://orcid.org/0000-0002-6069-8629>)
LicenseMIT + file LICENSE
URLhttps://github.com/PhilBoileau/scPCA
Bug Reportshttps://github.com/PhilBoileau/scPCA/issues
Downloads rank494
Source branchdevel
biocViewsDifferentialExpression, GeneExpression, Microarray, PrincipalComponent, RNASeq, Sequencing, Software

Documentation

Download

Follow the installation instructions to use this package in your R session.

Source packagescPCA_1.27.0.tar.gz
Windows binary (x86_64)scPCA_1.27.0.zip
macOS binary (arm64)scPCA_1.27.0.tgz
macOS binary (x86_64)scPCA_1.27.0.tgz
Dependencies

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

Suggests: DelayedMatrixStats, sparseMatrixStats, testthat (>= 2.1.0), covr, knitr, rmarkdown, BiocStyle, ggplot2, ggpubr, splatter, SingleCellExperiment, microbenchmark