<b> About </b> 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. <b> Installation </b> To install the R package: ```R # install.packages("devtools") devtools::install_github("wiscstatman/scDDboost") ``` A tutorial and examples can be found at Rpackage/vignette/ <b> Paper </b> Ma, X., Korthauer, K., Kendziorski, C., and Newton, M. A. (2019). A Compositional Model To Assess Expression Changes From Single-Cell RNA-Seq Data. The Annals of Applied Statistics 15, no. 2 (2021): 880-901. DOI: 10.1214/20-AOAS1423. Formerly, <a href="https://www.biorxiv.org/content/10.1101/655795v1.abstract"> bioRxiv 655795 </a>