DOI: 10.18129/B9.bioc.scPCA    

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

Sparse Contrastive Principal Component Analysis

Bioconductor version: Development (3.17)

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>

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


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biocViews DifferentialExpression, GeneExpression, Microarray, PrincipalComponent, RNASeq, Sequencing, Software
Version 1.13.0
In Bioconductor since BioC 3.10 (R-3.6) (3 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
Suggests DelayedMatrixStats, sparseMatrixStats, testthat (>= 2.1.0), covr, knitr, rmarkdown, BiocStyle, ggplot2, ggpubr, splatter, SingleCellExperiment, microbenchmark
Depends On Me OSCA.advanced, OSCA.workflows
Imports Me
Suggests Me
Links To Me
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