recipe bioconductor-corral

Correspondence Analysis for Single Cell Data

Homepage:

https://bioconductor.org/packages/3.16/bioc/html/corral.html

License:

GPL-2

Recipe:

/bioconductor-corral/meta.yaml

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 additional options, including variations of CA to address overdispersion in count data, as well as 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

Versions:

1.8.0-01.4.0-01.2.0-01.0.0-21.0.0-1

Depends:
Required By:

Installation

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 quay.io/biocontainers/bioconductor-corral:<tag>

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

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