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Marker Enrichment Modeling (MEM)

Bioconductor version: Release (3.19)

MEM, Marker Enrichment Modeling, automatically generates and displays quantitative labels for cell populations that have been identified from single-cell data. The input for MEM is a dataset that has pre-clustered or pre-gated populations with cells in rows and features in columns. Labels convey a list of measured features and the features' levels of relative enrichment on each population. MEM can be applied to a wide variety of data types and can compare between MEM labels from flow cytometry, mass cytometry, single cell RNA-seq, and spectral flow cytometry using RMSD.

Author: Sierra Lima [aut] , Kirsten Diggins [aut] , Jonathan Irish [aut, cre]

Maintainer: Jonathan Irish < at>

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


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:

Intro_to_Marker_Enrichment_Modeling_Analysis HTML R Script
Reference Manual PDF


biocViews CellBiology, Classification, Clustering, DataImport, DataRepresentation, FlowCytometry, Proteomics, SingleCell, Software, SystemsBiology
Version 1.8.0
In Bioconductor since BioC 3.15 (R-4.2) (2 years)
License GPL-3
Depends R (>= 4.2.0)
Imports gplots, tools, flowCore, grDevices, stats, utils, matrixStats, methods
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Suggests knitr, rmarkdown
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Follow Installation instructions to use this package in your R session.

Source Package cytoMEM_1.8.0.tar.gz
Windows Binary
macOS Binary (x86_64) cytoMEM_1.8.0.tgz
macOS Binary (arm64) cytoMEM_1.8.0.tgz
Source Repository git clone
Source Repository (Developer Access) git clone
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