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Michaelis-Menten Modelling of Dropouts in single-cell RNASeq

Bioconductor version: Release (3.19)

This package fits a model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. Also includes an method for calculating exact Pearson residuals in UMI-tagged data using a library-size aware negative binomial model.

Author: Tallulah Andrews <tallulandrews at>

Maintainer: Tallulah Andrews <tallulandrews at>

Citation (from within R, enter citation("M3Drop")):


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:

Introduction to M3Drop PDF R Script
Reference Manual PDF


biocViews DifferentialExpression, DimensionReduction, FeatureExtraction, GeneExpression, RNASeq, Sequencing, Software, Transcriptomics
Version 1.30.0
In Bioconductor since BioC 3.4 (R-3.3) (7.5 years)
License GPL (>=2)
Depends R (>= 3.4), numDeriv
Imports RColorBrewer, gplots, bbmle, statmod, grDevices, graphics, stats, matrixStats, Matrix, irlba, reldist, Hmisc, methods, scater
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Suggests ROCR, knitr, M3DExampleData, SingleCellExperiment, Seurat, Biobase
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Follow Installation instructions to use this package in your R session.

Source Package M3Drop_1.30.0.tar.gz
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macOS Binary (x86_64) M3Drop_1.30.0.tgz
macOS Binary (arm64) M3Drop_1.30.0.tgz
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Source Repository (Developer Access) git clone
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