Package: scPCA 1.21.1

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_1.21.1.tar.gz
scPCA_1.21.1.zip(r-4.5)scPCA_1.21.1.zip(r-4.4)scPCA_1.21.1.zip(r-4.3)
scPCA_1.21.1.tgz(r-4.5-any)scPCA_1.21.1.tgz(r-4.4-any)scPCA_1.21.1.tgz(r-4.3-any)
scPCA_1.21.1.tar.gz(r-4.5-noble)scPCA_1.21.1.tar.gz(r-4.4-noble)
scPCA_1.21.1.tgz(r-4.4-emscripten)scPCA_1.21.1.tgz(r-4.3-emscripten)
scPCA.pdf |scPCA.html✨
scPCA/json (API)
NEWS

# Install 'scPCA' in R:
install.packages('scPCA', repos = c('https://bioc.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-1.21.1(bioc 3.21)scPCA-1.20.0(bioc 3.20)

principalcomponentgeneexpressiondifferentialexpressionsequencingmicroarrayrnaseqbioconductorcontrastive-learningdimensionality-reduction

5.94 score 12 stars 29 scripts 254 downloads 1 exports 63 dependencies

Last updated 2 months agofrom:4329b9a777. Checks:1 OK, 8 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 13 2025
R-4.5-winNOTEMar 13 2025
R-4.5-macNOTEMar 13 2025
R-4.5-linuxNOTEMar 13 2025
R-4.4-winNOTEMar 13 2025
R-4.4-macNOTEMar 13 2025
R-4.4-linuxNOTEMar 13 2025
R-4.3-winNOTEMar 13 2025
R-4.3-macNOTEMar 13 2025

Exports:scPCA

Dependencies:abindassertthatBHBiocGenericsBiocParallelcliclustercodetoolscoopcpp11crayondata.tableDelayedArraydigestdplyrelasticnetfansiformatRfutile.loggerfutile.optionsfuturefuture.applygenericsglobalsglueIRangeskernlablambda.rlarslatticelifecyclelistenvmagrittrMatrixMatrixGenericsmatrixStatsorigamiparallellypillarpkgconfigpurrrR6rbibutilsRcppRcppEigenRdpackrlangRSpectrarsvdS4ArraysS4VectorsScaledMatrixsnowSparseArraysparsepcastringistringrtibbletidyselectutf8vctrswithrXVector

scPCA: Sparse contrastive principal component analysis

Philippe Boileau and Nima Hejazi

Rendered fromscpca_intro.Rmdusingknitr::rmarkdownon Mar 13 2025.

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