scPCA
Sparse Contrastive Principal Component Analysis
Bioconductor version: 3.23 · Package version: 1.26.0
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
| 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 |
| Downloads rank | 330 |
| Source branch | RELEASE_3_23 |
| biocViews | DifferentialExpression, GeneExpression, Microarray, PrincipalComponent, RNASeq, Sequencing, Software |
Documentation
Download
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