recipe bioconductor-zinbwave

Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data.

Homepage

https://bioconductor.org/packages/3.9/bioc/html/zinbwave.html

License

Artistic-2.0

Recipe

/bioconductor-zinbwave/meta.yaml

Links

biotools: zinbwave, doi: 10.1038/s41467-017-02554-5

package bioconductor-zinbwave

(downloads) docker_bioconductor-zinbwave

Versions

1.4.0-0, 1.2.0-0, 1.0.0-0

Depends bioconductor-biocparallel

>=1.16.0,<1.17.0

Depends bioconductor-edger

>=3.24.0,<3.25.0

Depends bioconductor-genefilter

>=1.64.0,<1.65.0

Depends bioconductor-singlecellexperiment

>=1.4.0,<1.5.0

Depends bioconductor-summarizedexperiment

>=1.12.0,<1.13.0

Depends r-base

>=3.5.1,<3.5.2.0a0

Depends r-copula

Depends r-glmnet

Depends r-matrix

Depends r-softimpute

Requirements

Installation

With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-zinbwave

and update with:

conda update bioconductor-zinbwave

or use the docker container:

docker pull quay.io/biocontainers/bioconductor-zinbwave:<tag>

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