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 nonnegative, countbased data that are in the same additive scale. The corral package implements CA for dimensionality reduction of a single matrix of singlecell data, as well as a multitable adaptation of CA that leverages dataoptimized 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 additional options, including variations of CA to address overdispersion in count data (e.g., FreemanTukey chisquared residual), as well as the option to apply CAstyle processing to continuous data (e.g., proteomic TOF intensities) with the Hellinger distance adaptation of CA.
Package details 


Bioconductor views  BatchEffect DimensionReduction GeneExpression Preprocessing PrincipalComponent Sequencing SingleCell Software Visualization 
Maintainer  
License  GPL2 
Version  1.9.1 
Package repository  View on GitHub 
Installation 
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