scPCA
Sparse Contrastive Principal Component Analysis
Bioconductor version: 3.23 · Package version: 1.26.0
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
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
| Maintainer | Philippe Boileau <philippe_boileau@berkeley.edu> |
| Author | Philippe 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>) |
| License | MIT + file LICENSE |
| URL | https://github.com/PhilBoileau/scPCA |
| Bug Reports | https://github.com/PhilBoileau/scPCA/issues |
| Source branch | RELEASE_3_23 |
| Build report | Bioconductor build system, r-universe |
| biocViews | DifferentialExpression, GeneExpression, Microarray, PrincipalComponent, RNASeq, Sequencing, Software |
| Package Short Url | https://bioconductor.org/packages/scPCA/ |
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 1.26.0, https://bioconductor.org/packages/scPCA.
Generated from the package metadata; it may differ from the package's own citation.
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | scPCA_1.26.0.tar.gz |
| Windows binary (x86_64) | scPCA_1.26.0.zip |
| macOS binary (arm64) | scPCA_1.26.0.tgz |
| macOS binary (x86_64) | scPCA_1.26.0.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