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 63 dependencies

Last updated 3 months agofrom:5acfbe2011. Checks:1 OK, 6 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKDec 30 2024
R-4.5-winNOTEDec 31 2024
R-4.5-linuxNOTEDec 30 2024
R-4.4-winNOTEDec 31 2024
R-4.4-macNOTEDec 30 2024
R-4.3-winNOTEDec 31 2024
R-4.3-macNOTEDec 30 2024

Exports:scPCA

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

scPCA: Sparse contrastive principal component analysis

Rendered fromscpca_intro.Rmdusingknitr::rmarkdownon Dec 30 2024.

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