Package: scPCA 1.21.0

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_1.21.0.tar.gz
scPCA_1.21.0.zip(r-4.5)scPCA_1.21.0.zip(r-4.4)scPCA_1.21.0.zip(r-4.3)
scPCA_1.21.0.tgz(r-4.4-any)scPCA_1.21.0.tgz(r-4.3-any)
scPCA_1.21.0.tar.gz(r-4.5-noble)scPCA_1.21.0.tar.gz(r-4.4-noble)
scPCA_1.21.0.tgz(r-4.4-emscripten)scPCA_1.21.0.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'))

Peer review:

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-1.21.0(bioc 3.21)scPCA-1.20.0(bioc 3.20)

principalcomponentgeneexpressiondifferentialexpressionsequencingmicroarrayrnaseqbioconductorcontrastive-learningdimensionality-reduction

6.24 score 12 stars 29 scripts 204 downloads 1 exports 64 dependencies

Last updated 2 months agofrom:5acfbe2011. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 30 2024
R-4.5-winNOTENov 30 2024
R-4.5-linuxNOTENov 30 2024
R-4.4-winNOTENov 30 2024
R-4.4-macNOTENov 30 2024
R-4.3-winNOTENov 30 2024
R-4.3-macNOTENov 30 2024

Exports:scPCA

Dependencies:abindassertthatBHBiocGenericsBiocParallelcliclustercodetoolscoopcpp11crayondata.tableDelayedArraydigestdplyrelasticnetfansiformatRfutile.loggerfutile.optionsfuturefuture.applygenericsglobalsglueIRangeskernlablambda.rlarslatticelifecyclelistenvmagrittrMatrixMatrixGenericsmatrixStatsorigamiparallellypillarpkgconfigpurrrR6rbibutilsRcppRcppEigenRdpackrlangRSpectrarsvdS4ArraysS4VectorsScaledMatrixsnowSparseArraysparsepcastringistringrtibbletidyselectutf8vctrswithrXVectorzlibbioc

scPCA: Sparse contrastive principal component analysis

Philippe Boileau and Nima Hejazi

Rendered fromscpca_intro.Rmdusingknitr::rmarkdownon Nov 30 2024.

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