CytoMethIC
Bioc currentDNA methylation-based machine learning models
Release Lineage
Entered 3.19 · May 1, 2024
Current · Requires R 4.6
Description
This package provides model data and functions for easily using machine learning models that use data from the DNA methylome to classify cancer type and phenotype from a sample. The primary motivation for the development of this package is to abstract away the granular and accessibility-limiting code required to utilize machine learning models in R. Our package provides this abstraction for RandomForest, e1071 Support Vector, Extreme Gradient Boosting, and Tensorflow models. This is paired with an ExperimentHub component, which contains models developed for epigenetic cancer classification and predicting phenotypes. This includes CNS tumor classification, Pan-cancer classification, race prediction, cell of origin classification, and subtype classification models. The package links to our models on ExperimentHub. The package currently supports HM450, EPIC, EPICv2, MSA, and MM285.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
9 4 exported
Complexity
4.6 avg / 14 max
Call network
9 nodes / 6 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
877
Files
29
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
5
Internal functions
5
Recent export changes
Testing & CI
Has tests
Yes
Test-to-code ratio
0.08
testthat edition
–
CI present
No
CI type
[]
PR gated
No
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
–
Unsafe pattern score
0
Dep constraint coverage
0%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
4.4.0
System requirements
–
C++ standard
–
License
Artistic-2.0
License flags
SPDX valid, OSI approved
History
Versions
5
First release
2024-04-30
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
–
Dep drift
1
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 0%
Topics
People
- Jacob Fanale author maintainer
- Wanding Zhou author
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("CytoMethIC")This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-08-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.