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] (<https://orcid.org/0000-0002-6035-7381>), Aedin Culhane [aut] (<https://orcid.org/0000-0002-1395-9734>)|
|Bioconductor views||BatchEffect DimensionReduction Preprocessing PrincipalComponent Sequencing SingleCell Software Visualization|
|Maintainer||Lauren Hsu <firstname.lastname@example.org>|
|Package repository||View on Bioconductor|
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