sccomp
Bioc currentDifferential Composition and Variability Analysis for Single-Cell Data
Release Lineage
Entered 3.15 · Apr 27, 2022
Current · Requires R 4.6
Description
Comprehensive R package for differential composition and variability analysis in single-cell RNA sequencing, CyTOF, and microbiome data. Provides robust Bayesian modeling with outlier detection, random effects, and advanced statistical methods for cell type proportion analysis. Features include probabilistic outlier identification, mixed-effect modeling, differential variability testing, and comprehensive visualization tools. Perfect for cancer research, immunology, developmental biology, and single-cell genomics applications.
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
93 15 exported
Complexity
3.3 avg / 22 max
Call network
93 nodes / 148 edges
Test coverage is not measured for Bioconductor packages; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
Code
Structure
Lines of code
13,515
Files
87
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
16
Internal functions
75
Recent export changes
Testing
Has tests
Yes
Test-to-code ratio
0.44
testthat edition
3
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
0%
Unsafe pattern score
0
Dep constraint coverage
4.2%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
4.3.0
System requirements
2 externalCmdStan (https://mc-stan.org/users/interfaces/cmdstan), C++14
C++ standard
–
License
GPL-3
License flags
SPDX valid, OSI approved
History
Versions
9
First release
2022-04-26
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
100%
Dep drift
20
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Help pages
- 24
- Examples that run
- 84%16 run in a plain check · 2 only when donttest examples are enabled (R CMD check --as-cran) · 1 hold no code
- Exported functions with examples
- 94%
- Documented parameters
- 95%
- Return-value docs
- 100%
- Help pages citing references
- 50%
Repository practices
6 development-tooling and community-health practices detected across 3 families in the upstream repository
Checks run against github.com/mangiolalaboratory/sccomp on 2026-10-21.
The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel
GitHub Pages site last deployed 2026-10-06 · The repository has a site deploy workflow
Datasets
| Name | Class | Size | Also in |
|---|---|---|---|
| counts_obj | tibble | 720 × 6 | No other package |
| no_significance_df | tibble | 34 × 4 | No other package |
| sce_obj | – | – | – |
| seurat_obj | – | – | – |
Topics
People
- Stefano Mangiola author maintainer
- Zijie Gao author
- Mengyao Ma author
- Anthony T. Papenfuss author
- Alexandra J. Roth-Schulze author
- Alan F. Rubin author
- Heejung Shim author
- Terence P. Speed author
- Marie Trussart author
- Enrique Zozaya-Valdés author
Cite
Cite this package
Run in R for the authors' entry:
citation("sccomp")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1073/pnas.2203828120.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.