gage
Bioc currentGenerally Applicable Gene-set Enrichment for Pathway Analysis
Release Lineage
Entered 2.7 · Oct 18, 2010
Current · Requires R 4.6
Description
GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These funtions and data are also useful for gene set analysis using other methods.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
31 20 exported
Complexity
12.3 avg / 103 max
Call network
31 nodes / 21 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
5,543
Files
70
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
20
Internal functions
10
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
40%
Unsafe pattern score
3
Dep constraint coverage
0%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
2.10
System requirements
–
C++ standard
–
License
GPL (>=2.0)
License flags
not SPDX, not OSI
History
Versions
32
First release
2010-10-18
Latest release
2026-04-28
Avg cadence
181 days
Cold removal rate
–
Dep drift
4
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
- 94%
- Return-value docs
- 100%
- References docs
- 94%
Topics
Depended on by (8)
People
Weijun Luo
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("gage")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.