ramwas
Bioc currentFast Methylome-Wide Association Study Pipeline for Enrichment Platforms
Release Lineage
Entered 3.5 · Apr 25, 2017
Current · Requires R 4.6
Description
A complete toolset for methylome-wide association studies (MWAS). It is specifically designed for data from enrichment based methylation assays, but can be applied to other data as well. The analysis pipeline includes seven steps: (1) scanning aligned reads from BAM files, (2) calculation of quality control measures, (3) creation of methylation score (coverage) matrix, (4) principal component analysis for capturing batch effects and detection of outliers, (5) association analysis with respect to phenotypes of interest while correcting for top PCs and known covariates, (6) annotation of significant findings, and (7) multi-marker analysis (methylation risk score) using elastic net. Additionally, RaMWAS include tools for joint analysis of methlyation and genotype data. This work is published in Bioinformatics, Shabalin et al. (2018) <doi:10.1093/bioinformatics/bty069>.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
132 55 exported
Complexity
4 avg / 67 max
Call network
132 nodes / 180 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
11,093
Files
75
Compiled share
1.1%
Has compiled src
Yes
Language breakdown
API
Exported functions
55
Internal functions
71
Recent export changes
Testing & CI
Has tests
No
Test-to-code ratio
0.00
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
3.3.0
System requirements
–
C++ standard
–
License
LGPL-3
License flags
SPDX valid, OSI approved
History
Versions
19
First release
2017-04-24
Latest release
2026-04-28
Avg cadence
183 days
Cold removal rate
100%
Dep drift
0
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Examples that run
- 93%
- Documented parameters
- 98%
- Return-value docs
- 100%
- References docs
- 0%
Topics
People
- Andrey A Shabalin author maintainer
- Karolina A Aberg author
- Shaunna L Clark author
- Mohammad W Hattab author
- Edwin J C G van den Oord author