COCOA
Bioc currentCoordinate Covariation Analysis
Release Lineage
Entered 3.8 · Oct 31, 2018
Current · Requires R 4.6
Description
COCOA is a method for understanding epigenetic variation among samples. COCOA can be used with epigenetic data that includes genomic coordinates and an epigenetic signal, such as DNA methylation and chromatin accessibility data. To describe the method on a high level, COCOA quantifies inter-sample variation with either a supervised or unsupervised technique then uses a database of "region sets" to annotate the variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance transcription factor (TF) binding regions, histone modification regions, or open chromatin regions. COCOA can identify region sets that are associated with epigenetic variation between samples and increase understanding of variation in your data.
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
41 14 exported
Complexity
7.4 avg / 30 max
Call network
41 nodes / 61 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
8,183
Files
54
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
14
Internal functions
27
Recent export changes
Testing
Has tests
Yes
Test-to-code ratio
0.08
testthat edition
–
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
0%
Unsafe pattern score
39
Dep constraint coverage
0%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
3.5
System requirements
–
C++ standard
–
License
GPL-3
License flags
SPDX valid, OSI approved
History
Versions
16
First release
2019-03-06
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
100%
Dep drift
2
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Help pages
- 27includes a package overview page
- Examples that run
- 93%13 run in a plain check · 1 hold no code
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 4%
Repository practices
2 development-tooling and community-health practices detected across 2 families in the upstream repository
Checks run against github.com/databio/cocoa on 2026-10-22.
Datasets
| Name | Class | Size | Also in |
|---|---|---|---|
| atf3_chr1 | – | – | – |
| brcaATACCoord1 | – | – | – |
| brcaATACData1 | matrix | 4,053 × 37 | No other package |
| brcaMCoord1 | – | – | – |
| brcaMetadata | data.frame | 329 × 6 | No other package |
| brcaMethylData1 | matrix | 6,004 × 300 | No other package |
| brcaPCScores | matrix | 4 × 4 | No other package |
| brcaPCScores657 | data.frame | 657 × 5 | No other package |
| esr1_chr1 | – | – | – |
| gata3_chr1 | – | – | – |
| nrf1_chr1 | – | – | – |
| rsScores | data.frame | 5 × 3 | No other package |
Topics
People
- John Lawson author maintainer
-
Nathan Sheffield
author
http://www.databio.org
- Jason Smith contributor
Cite
Cite this package
Run in R for the authors' entry:
citation("COCOA")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1186/s13059-020-02139-4.
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.