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.
To install the R package:
# install.packages("devtools") devtools::install_github("wiscstatman/scDDboost")
A tutorial and examples can be found at Rpackage/vignette/scDDb.pdf.
Ma, X., Korthauer, K., Kendziorski, C., and Newton, M. A. (2019). A Compositional Model To Assess Expression Changes From Single-Cell RNA-Seq Data. bioRxiv 655795