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This is the development version of slalom; for the stable release version, see slalom.

Factorial Latent Variable Modeling of Single-Cell RNA-Seq Data

Bioconductor version: Development (3.20)

slalom is a scalable modelling framework for single-cell RNA-seq data that uses gene set annotations to dissect single-cell transcriptome heterogeneity, thereby allowing to identify biological drivers of cell-to-cell variability and model confounding factors. The method uses Bayesian factor analysis with a latent variable model to identify active pathways (selected by the user, e.g. KEGG pathways) that explain variation in a single-cell RNA-seq dataset. This an R/C++ implementation of the f-scLVM Python package. See the publication describing the method at

Author: Florian Buettner [aut], Naruemon Pratanwanich [aut], Davis McCarthy [aut, cre], John Marioni [aut], Oliver Stegle [aut]

Maintainer: Davis McCarthy <davis at>

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


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

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

# The following initializes usage of Bioc devel


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 slalom HTML R Script
Reference Manual PDF


biocViews DimensionReduction, GeneExpression, ImmunoOncology, KEGG, Normalization, RNASeq, Reactome, Sequencing, SingleCell, Software, Transcriptomics, Visualization
Version 1.27.0
In Bioconductor since BioC 3.6 (R-3.4) (6.5 years)
License GPL-2
Depends R (>= 4.0)
Imports Rcpp (>= 0.12.8), RcppArmadillo, BH, ggplot2, grid, GSEABase, methods, rsvd, SingleCellExperiment, SummarizedExperiment, stats
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Suggests BiocStyle, knitr, rhdf5, rmarkdown, scater, testthat
Linking To Rcpp, RcppArmadillo, BH
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