singleCellTK
Bioc currentComprehensive and Interactive Analysis of Single Cell RNA-Seq Data
Release Lineage
Entered 3.7 · May 1, 2018
Current · Requires R 4.6
Description
The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk.
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
390 237 exported
Complexity
6.7 avg / 61 max
Call network
390 nodes / 494 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
49,529
Files
505
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
252
Internal functions
148
Recent export changes
Testing
Has tests
Yes
Test-to-code ratio
0.04
testthat edition
–
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
0%
Unsafe pattern score
18
Dep constraint coverage
10.1%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
4.0
System requirements
–
C++ standard
–
License
MIT + file LICENSE
License flags
SPDX valid, OSI approved
History
Versions
17
First release
2018-06-20
Latest release
2026-04-28
Avg cadence
181 days
Cold removal rate
100%
Dep drift
83
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Help pages
- 238
- Examples that run
- 95%196 run in a plain check · 10 never run
- Exported functions with examples
- 86%
- Documented parameters
- 99%
- Return-value docs
- 100%
- Help pages citing references
- 4%
Repository practices
10 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/compbiomed/singlecelltk on 2026-10-11.
The R CMD check workflow runs on Linux, macOS and Windows
GitHub Pages site last deployed 2018-03-29
Datasets
| Name | Class | Size | Also in |
|---|---|---|---|
| MitoGenes | list | 8 elements | No other package |
| SEG | list | 2 elements | No other package |
| cell.counts | list | 10 elements | No other package |
| empty_drops_result | data.frame | 1,000 × 7 | No other package |
| genes.barcodes | data.frame | 19 × 1 | No other package |
| genes.genes | data.frame | 19 × 1 | No other package |
| merged-cell-metrics | – | – | – |
| merged-gene-metrics | – | – | – |
| mouseBrainSubsetSCE | – | – | – |
| msigdb_table | data.frame | 17 × 5 | No other package |
| pbmc_1k_sparse_counts_barcodes | data.frame | 49 × 2 | No other package |
| pbmc_1k_sparse_counts_genes | data.frame | 49 × 2 | No other package |
| sce | – | – | – |
| sceBatches | – | – | – |
1 internal object is bundled for the package's own use and not listed here.
Topics
Suggested by (1)
People
- Joshua David Campbell author maintainer
- Vidya Akavoor author
- Salam Alabdullatif author
- Shruthi Bandyadka author
- Xinyun Cao author
- Tyler Faits author
- Rui Hong author
- David Jenkins author
- W. Evan Johnson author
- Mohammed Muzamil Khan author
- Yusuke Koga author
- Anastasia Leshchyk author
- Ming Liu author
- Nida Pervaiz author
- Irzam Sarfraz author
- Yichen Wang author
- Zhe Wang author
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("singleCellTK")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 v2026-10-19, which the citation names so these numbers can be found later. More on citing and the projects behind them.