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Robust Outlier-aware Estimation of Composition and Heterogeneity for Single-cell Data

Bioconductor version: Development (3.19)

A robust and outlier-aware method for testing differential tissue composition from single-cell data. This model can infer changes in tissue composition and heterogeneity, and can produce realistic data simulations based on any existing dataset. This model can also transfer knowledge from a large set of integrated datasets to increase accuracy further.

Author: Stefano Mangiola [aut, cre]

Maintainer: Stefano Mangiola <mangiolastefano at gmail.com>

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


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:

New sccomp framework HTML R Script
Overview of the sccomp package HTML R Script
Reference Manual PDF


biocViews Clustering, GeneExpression, ImmunoOncology, Normalization, RNASeq, Sequencing, SingleCell, Software, Transcriptomics
Version 1.7.5
In Bioconductor since BioC 3.15 (R-4.2) (2 years)
License GPL-3
Depends R (>= 4.2.0)
Imports methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstantools (>= 2.1.1), rstan (>= 2.26.0), SeuratObject, SingleCellExperiment, parallel, dplyr, tidyr, purrr, magrittr, rlang, tibble, boot, lifecycle, stats, tidyselect, utils, ggplot2, ggrepel, patchwork, forcats, readr, scales, stringr, glue
System Requirements GNU make
URL https://github.com/stemangiola/sccomp
Bug Reports https://github.com/stemangiola/sccomp/issues
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Suggests BiocStyle, testthat (>= 3.0.0), markdown, knitr, loo, tidyseurat, tidySingleCellExperiment
Linking To BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>=, RcppParallel (>= 5.0.1), rstan (>= 2.26.0), StanHeaders (>= 2.26.0)
Enhances furrr, extraDistr
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Package Archives

Follow Installation instructions to use this package in your R session.

Source Package sccomp_1.7.5.tar.gz
Windows Binary sccomp_1.7.5.zip
macOS Binary (x86_64) sccomp_1.7.5.tgz
macOS Binary (arm64) sccomp_1.7.2.tgz
Source Repository git clone https://git.bioconductor.org/packages/sccomp
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/sccomp
Bioc Package Browser https://code.bioconductor.org/browse/sccomp/
Package Short Url https://bioconductor.org/packages/sccomp/
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