A Toolkit for Statistical Analysis of Single-Cell Omics Data
Overview
A suite of statistical methods for analysis of single-cell omics data including linear model-based methods for differential abundance analysis for individual level single-cell RNA-seq data. For more details see Zhang, et al. (Submitted to Bioinformatics)https://github.com/Lujun995/DiSC_Replication_Code.
Install
Health
NOTEdependencies in R coder-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Namespace in Imports field not imported from: ‘vegan’ All declared Imports should be used.
- NOTE11 OK, 2 NOTENow
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 2
- Examples that run
- 100%2 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 50%
Downloads
Dependencies
Installing SingleCellStat adds 3 packages (2 with compiled code, 1.7 MB of source tarballs). Heaviest: vegan 1.4 MB, matrixStats 207 KB, permute 118 KB.
Code & Tests
Datasets
People & History
- RR 4.6.0 released · 2026-04-24
- 0.3.1Latest2025-05-13 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-05-13
- Total releases
- 1 / 1 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 4.3.0
- Bundled data
- 2.4 MB / 1 file
- Download size
- 2.5 MB
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("SingleCellStat")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
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.