- recipe bioconductor-corral
Correspondence Analysis for Single Cell Data
- Homepage:
https://bioconductor.org/packages/3.14/bioc/html/corral.html
- License:
GPL-2
- Recipe:
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
- 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>
)
Download stats¶
Link to this page¶
Render an badge with the following MarkDown:
[](http://bioconda.github.io/recipes/bioconductor-corral/README.html)