recipe bioconductor-corral

Correspondence Analysis for Single Cell Data

Homepage

https://bioconductor.org/packages/3.14/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 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

Versions

1.4.0-0,  1.2.0-0,  1.0.0-2,  1.0.0-1

Depends

• bioconductor-multiassayexperiment >=1.20.0,<1.21.0

• bioconductor-singlecellexperiment >=1.16.0,<1.17.0

• bioconductor-summarizedexperiment >=1.24.0,<1.25.0

• r-base >=4.1,<4.2.0a0

• r-ggplot2

• r-ggthemes

• r-gridextra

• r-irlba

• r-matrix

• r-pals

• r-transport

Required By

Installation

With an activated Bioconda channel (see 2. 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>)

Download stats

Link to this page

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