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

Bioconductor version: Release (3.19)

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.

Author: Xiuyu Ma [cre, aut], Michael A. Newton [ctb]

Maintainer: Xiuyu Ma <watsonforfun at>

Citation (from within R, enter citation("scDDboost")):


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scDDboost Tutorial HTML R Script
Reference Manual PDF


biocViews Bayesian, Clustering, DifferentialExpression, GeneExpression, Sequencing, SingleCell, Software
Version 1.6.0
In Bioconductor since BioC 3.16 (R-4.2) (1.5 years)
License GPL (>= 2)
Depends R (>= 4.2), ggplot2
Imports Rcpp (>= 0.12.11), RcppEigen (>=, EBSeq, BiocParallel, mclust, SingleCellExperiment, cluster, Oscope, SummarizedExperiment, stats, methods
System Requirements c++11
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