cola
Bioc currentA Framework for Consensus Partitioning
Release Lineage
Entered 3.9 · May 3, 2019
Current · Requires R 4.6
Description
Subgroup classification is a basic task in genomic data analysis, especially for gene expression and DNA methylation data analysis. It can also be used to test the agreement to known clinical annotations, or to test whether there exist significant batch effects. The cola package provides a general framework for subgroup classification by consensus partitioning. It has the following features: 1. It modularizes the consensus partitioning processes that various methods can be easily integrated. 2. It provides rich visualizations for interpreting the results. 3. It allows running multiple methods at the same time and provides functionalities to straightforward compare results. 4. It provides a new method to extract features which are more efficient to separate subgroups. 5. It automatically generates detailed reports for the complete analysis. 6. It allows applying consensus partitioning in a hierarchical manner.
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
125 45 exported
Complexity
6.2 avg / 84 max
Call network
125 nodes / 124 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
19,400
Files
242
Compiled share
1.4%
Has compiled src
Yes
Language breakdown
API
Exported functions
49
Internal functions
67
Recent export changes
Testing
Has tests
Yes
Test-to-code ratio
0.02
testthat edition
–
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
42.1%
Unsafe pattern score
3
Dep constraint coverage
20.6%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
4.0.0
System requirements
–
C++ standard
–
License
MIT + file LICENSE
License flags
SPDX valid, OSI approved
History
Versions
15
First release
2019-09-16
Latest release
2026-04-28
Avg cadence
180 days
Cold removal rate
100%
Dep drift
4
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Help pages
- 175
- Examples that run
- 92%161 run in a plain check · 10 only when donttest examples are enabled (R CMD check --as-cran) · 4 never run
- Exported functions with examples
- 100%
- Documented parameters
- 97%
- Return-value docs
- 63%
- Help pages citing references
- 0%
- The cola package HTML
Repository practices
4 development-tooling and community-health practices detected across 2 families in the upstream repository
Checks run against github.com/jokergoo/cola on 2026-10-18.
The R CMD check workflow runs on Linux, macOS and Windows
GitHub Pages site last deployed 2026-01-30
Datasets
| Name | Class | Size | Also in |
|---|---|---|---|
| cola_rl | – | – | – |
| consensus_mat_list | list | 2 elements | No other package |
| golub_cola | – | – | – |
| golub_cola_ds | – | – | – |
| golub_cola_rh | – | – | – |
Topics
Suggested by (2)
People
- Zuguang Gu author maintainer
Cite
Cite this package
Run in R for the authors' entry:
citation("cola")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1093/nar/gkaa1146, 10.1093/bib/bbac048.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.