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Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions

Bioconductor version: Release (3.19)

This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions.

Author: Keegan Korthauer [cre, aut]

Maintainer: Keegan Korthauer <keegan at>

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


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

scDD Quickstart PDF R Script
Reference Manual PDF


biocViews Bayesian, Clustering, DifferentialExpression, ImmunoOncology, MultipleComparison, RNASeq, SingleCell, Software, Visualization
Version 1.28.0
In Bioconductor since BioC 3.5 (R-3.4) (7 years)
License GPL-2
Depends R (>= 3.5.0)
Imports fields, mclust, BiocParallel, outliers, ggplot2, EBSeq, arm, SingleCellExperiment, SummarizedExperiment, grDevices, graphics, stats, S4Vectors, scran
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Follow Installation instructions to use this package in your R session.

Source Package scDD_1.28.0.tar.gz
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macOS Binary (x86_64) scDD_1.28.0.tgz
macOS Binary (arm64) scDD_1.28.0.tgz
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