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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.
Package details |
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Author | Philippe Boileau [aut, cre, cph] (<https://orcid.org/0000-0002-4850-2507>), Nima Hejazi [aut] (<https://orcid.org/0000-0002-7127-2789>), Sandrine Dudoit [ctb, ths] (<https://orcid.org/0000-0002-6069-8629>) |
Bioconductor views | DifferentialExpression GeneExpression Microarray PrincipalComponent RNASeq Sequencing |
Maintainer | Philippe Boileau <philippe_boileau@berkeley.edu> |
License | MIT + file LICENSE |
Version | 1.4.0 |
URL | https://github.com/PhilBoileau/scPCA |
Package repository | View on Bioconductor |
Installation |
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