recipe bioconductor-corral

Correspondence Analysis for Single Cell Data






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.

package bioconductor-corral

(downloads) docker_bioconductor-corral



Required By:


With an activated Bioconda channel (see set-up-channels), install with:

conda install bioconductor-corral

and update with:

conda update bioconductor-corral

or use the docker container:

docker pull<tag>

(see bioconductor-corral/tags for valid values for <tag>)

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