About

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.

Installation

To install the R package:

# install.packages("devtools")
devtools::install_github("wiscstatman/scDDboost")

A tutorial and examples can be found at Rpackage/vignette/

Paper

Ma, X., Korthauer, K., Kendziorski, C., and Newton, M. A. (2021). A Compositional Model To Assess Expression Changes From Single-Cell RNA-Seq Data. The Annals of Applied Statistics 15, no. 2: 880-901. https://doi.org/10.1214/20-AOAS1423 . Earlier version, bioRxiv 655795