npGSEA
Bioc currentPermutation approximation methods for gene set enrichment analysis (non-permutation GSEA)
Release Lineage
Entered 2.14 · Apr 14, 2014
Current · Requires R 4.6
Description
Current gene set enrichment methods rely upon permutations for inference. These approaches are computationally expensive and have minimum achievable p-values based on the number of permutations, not on the actual observed statistics. We have derived three parametric approximations to the permutation distributions of two gene set enrichment test statistics. We are able to reduce the computational burden and granularity issues of permutation testing with our method, which is implemented in this package. npGSEA calculates gene set enrichment statistics and p-values without the computational cost of permutations. It is applicable in settings where one or many gene sets are of interest. There are also built-in plotting functions to help users visualize results.
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
21 2 exported
Complexity
3 avg / 17 max
Call network
21 nodes / 24 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
2,459
Files
42
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
2
Internal functions
19
Testing
Has tests
No
Test-to-code ratio
0.00
testthat edition
–
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
–
Unsafe pattern score
0
Dep constraint coverage
16.7%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
–
System requirements
–
C++ standard
–
License
Artistic-2.0
License flags
SPDX valid, OSI approved
History
Versions
25
First release
2014-04-11
Latest release
2026-04-28
Avg cadence
183 days
Cold removal rate
–
Dep drift
0
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Help pages
- 25
- Examples that run
- 100%25 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 32%
Repository practices
Checks read the Bioconductor git repository (git.bioconductor.org), branch RELEASE_3_23.
No development-tooling practices detected in the Bioconductor git repository.
Topics
People (from the free-text Author field)
Cite
Cite this package
Run in R for the authors' entry:
citation("npGSEA")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1186/s12859-015-0571-7.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-26, which the citation names so these numbers can be found later. More on citing and the projects behind them.