Package: scPCA 1.26.0

Philippe Boileau

scPCA: Sparse Contrastive Principal Component Analysis

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.

Authors:Philippe Boileau [aut, cre, cph], Nima Hejazi [aut], Sandrine Dudoit [ctb, ths]

scPCA_1.26.0.tar.gz
scPCA_1.26.0.zip(r-4.7)scPCA_1.26.0.zip(r-4.6)scPCA_1.26.0.zip(r-4.5)
scPCA_1.26.0.tgz(r-4.6-any)scPCA_1.26.0.tgz(r-4.5-any)
scPCA_1.26.0.tar.gz(r-4.7-any)scPCA_1.26.0.tar.gz(r-4.6-any)
scPCA_1.26.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
scPCA/json (API)
NEWS

# Install 'scPCA' in R:

install.packages('scPCA', repos = c('https://bioc-release.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/philboileau/scpca/issues

Datasets:

background_df - Simulated Background Data for cPCA and scPCA
toy_df - Simulated Target Data for cPCA and scPCA

On BioConductor:scPCA-1.27.0(bioc 3.24)scPCA-1.26.0(bioc 3.23)

principalcomponent geneexpression differentialexpression sequencing microarray rnaseq bioconductor contrastive-learning dimensionality-reduction

6.28 score 12 stars 32 scripts 406 downloads 1 exports 61 dependencies

Last updated from:0681c9497a (on RELEASE_3_23). Checks:1 WARNING, 7 NOTE, 2 OK. Indexed: no.

Target	Result	Time
bioc-checks	WARNING	204
linux-devel-x86_64	NOTE	352
source / vignettes	OK	406
linux-release-x86_64	NOTE	370
macos-release-arm64	NOTE	193
macos-oldrel-arm64	NOTE	183
windows-devel	NOTE	217
windows-release	NOTE	240
windows-oldrel	NOTE	202
wasm-release	OK	161

Exports:scPCA

Dependencies:abind assertthat BH BiocGenerics BiocParallel cli cluster codetools coop cpp11 data.table DelayedArray digest dplyr elasticnet formatR futile.logger futile.options future future.apply generics globals glue IRanges kernlab lambda.r lars lattice lifecycle listenv magrittr Matrix MatrixGenerics matrixStats origami parallelly pillar pkgconfig purrr R6 rbibutils Rcpp RcppEigen Rdpack rlang RSpectra rsvd S4Arrays S4Vectors ScaledMatrix snow SparseArray sparsepca stringi stringr tibble tidyselect utf8 vctrs withr XVector

scPCA: Sparse contrastive principal component analysis

Philippe Boileau and Nima Hejazi

Rendered fromscpca_intro.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2021-05-21
Started: 2019-05-02

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Simulated Background Data for cPCA and scPCA	background_df
Sparse Contrastive Principal Component Analysis	scPCA
Simulated Target Data for cPCA and scPCA	toy_df