goatea
Bioc currentInteractive Exploration of GSEA by the GOAT Method
Release Lineage
Entered 3.22 · Oct 30, 2025
Current · Requires R 4.6
Description
Geneset Ordinal Association Test Enrichment Analysis (GOATEA) provides a 'Shiny' interface with interactive visualizations and utility functions for performing and exploring automated gene set enrichment analysis using the 'GOAT' package. 'GOATEA' is designed to support large-scale and user-friendly enrichment workflows across multiple gene lists and comparisons, with flexible plotting and output options. Visualizations pre-enrichment include interactive 'Volcano' and 'UpSet' (overlap) plots. Visualizations post-enrichment include interactive geneset dotplot, geneset treeplot, gene-effectsize heatmap, gene-geneset heatmap and 'STRING' database of protein-protein-interactions network graph. 'GOAT' reference: Frank Koopmans (2024) <doi:10.1038/s42003-024-06454-5>.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
31 24 exported
Complexity
10.3 avg / 82 max
Call network
31 nodes / 29 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
6,487
Files
225
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
25
Internal functions
7
Recent export changes
Testing & CI
Has tests
No
Test-to-code ratio
0.00
testthat edition
3
CI present
Yes
CI type
["github-actions"]
PR gated
Yes
Docs
Roxygen coverage
96%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
25%
Unsafe pattern score
0
Dep constraint coverage
100%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
4.5.0
System requirements
–
C++ standard
–
License
Apache License (>= 2)
License flags
SPDX valid, OSI approved
History
Versions
2
First release
2026-02-17
Latest release
2026-04-28
Avg cadence
70 days
Cold removal rate
–
Dep drift
10
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
- 99%
- Return-value docs
- 100%
- References docs
- 3%
Topics
People
- Maurits Unkel author maintainer fnd cph