DOI: 10.18129/B9.bioc.corral    

Correspondence Analysis for Single Cell Data

Bioconductor version: Release (3.12)

Correspondence analysis (CA) is a matrix factorization method, and is similar to principal components analysis (PCA). Whereas PCA is designed for application to continuous, approximately normally distributed data, CA is appropriate for non-negative, count-based data that are in the same additive scale. The corral package implements CA for dimensionality reduction of a single matrix of single-cell data, as well as a multi-table adaptation of CA that leverages data-optimized scaling to align data generated from different sequencing platforms by projecting into a shared latent space. corral utilizes sparse matrices and a fast implementation of SVD, and can be called directly on Bioconductor objects (e.g., SingleCellExperiment) for easy pipeline integration. The package also includes the option to apply CA-style processing to continuous data (e.g., proteomic TOF intensities) with the Hellinger distance adaptation of CA.

Author: Lauren Hsu [aut, cre] , Aedin Culhane [aut]

Maintainer: Lauren Hsu <lrnshoe at>

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HTML R Script alignment with corralm
HTML R Script dim reduction with corral
PDF   Reference Manual


biocViews BatchEffect, DimensionReduction, Preprocessing, PrincipalComponent, Sequencing, SingleCell, Software, Visualization
Version 1.0.0
In Bioconductor since BioC 3.12 (R-4.0) (< 6 months)
License GPL-2
Imports ggplot2, ggthemes, grDevices, gridExtra, irlba, Matrix, methods, MultiAssayExperiment, pals, SingleCellExperiment, SummarizedExperiment, transport
Suggests ade4, BiocStyle, CellBench, DuoClustering2018, knitr, testthat
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