recipe bioconductor-scddboost

A compositional model to assess expression changes from single-cell rna-seq data

Homepage:

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

License:

GPL (>= 2)

Recipe:

/bioconductor-scddboost/meta.yaml

scDDboost is an R package to analyze changes in the distribution of single-cell expression data between two experimental conditions. Compared to other methods that assess differential expression, scDDboost benefits uniquely from information conveyed by the clustering of cells into cellular subtypes. Through a novel empirical Bayesian formulation it calculates gene-specific posterior probabilities that the marginal expression distribution is the same (or different) between the two conditions. The implementation in scDDboost treats gene-level expression data within each condition as a mixture of negative binomial distributions.

package bioconductor-scddboost

Versions:

1.0.0-0

Depends:

• bioconductor-biocparallel >=1.32.0,<1.33.0

• bioconductor-ebseq >=1.38.0,<1.39.0

• bioconductor-oscope >=1.28.0,<1.29.0

• bioconductor-singlecellexperiment >=1.20.0,<1.21.0

• bioconductor-summarizedexperiment >=1.28.0,<1.29.0

• libblas >=3.9.0,<4.0a0

• libgcc-ng >=12

• liblapack >=3.9.0,<4.0a0

• libstdcxx-ng >=12

• r-base >=4.2,<4.3.0a0

• r-bh

• r-cluster

• r-ggplot2

• r-mclust

• r-rcpp >=0.12.11

• r-rcppeigen >=0.3.2.9.0

Required By:

Installation

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

conda install bioconductor-scddboost

and update with:

conda update bioconductor-scddboost

or use the docker container:

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

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

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Link to this page

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