glmGamPoi: fitting Gamma-Poisson generalized linear models on single cell count data

benchmark of glmGamPoi vs DESeq2 and edgeR


Motivation: The Gamma-Poisson distribution is a theoretically and empirically motivated model for the sampling variability of single cell RNA-sequencing counts and an essential building block for analysis approaches including differential expression analysis, principal component analysis and factor analysis. Existing implementations for inferring its parameters from data often struggle with the size of single cell datasets, which can comprise millions of cells; at the same time, they do not take full advantage of the fact that zero and other small numbers are frequent in the data. These limitations have hampered uptake of the model, leaving room for statistically inferior approaches such as logarithm(-like) transformation.
Results: We present a new R package for fitting the Gamma-Poisson distribution to data with the characteristics of modern single cell datasets more quickly and more accurately than existing methods. The software can work with data on disk without having to load them into RAM simultaneously.
Availability: The package glmGamPoi is available from Bioconductor for Windows, macOS and Linux, and source code is available on under a GPL-3 license. The scripts to reproduce the results of this paper are available on

Oxford Bioinformatics