LimROTS
Bioc currentLimROTS: A Hybrid Method Integrating Empirical Bayes and Reproducibility-Optimized Statistics for Robust Differential Expression Analysis
Release Lineage
Entered 3.21 · Apr 16, 2025
Current · Requires R 4.6
Description
Differential expression analysis is commonly used to study diverse biological datasets. The reproducibility-optimized test statistic (ROTS) (Elo et al., 2008, <doi:10.1109/tcbb.2007.1078>) uses a modified t-statistic to prioritise features that differ between two or more groups. However, the ROTS Bioconductor implementation (Suomi et al., 2017, <doi:10.1371/journal.pcbi.1005562>) did not accommodate technical or biological covariates. LimROTS (Anwar et al., 2025, <doi:10.1093/bioinformatics/btaf570>) addressed this limitation by combining a reproducibility-optimized test statistic with the limma empirical Bayes approach (Ritchie et al., 2015, <doi:10.1093/nar/gkv007>). This enables the analysis of more complex experimental designs and the incorporation of covariates.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
22 2 exported
Complexity
6.9 avg / 19 max
Call network
22 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
5,767
Files
63
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
2
Internal functions
17
Recent export changes
Testing & CI
Has tests
Yes
Test-to-code ratio
0.17
testthat edition
3
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
4.5.0
System requirements
–
C++ standard
–
License
GPL (>= 2)
License flags
SPDX valid, OSI approved
History
Versions
3
First release
2025-06-16
Latest release
2026-04-28
Avg cadence
158 days
Cold removal rate
–
Dep drift
3
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
- 13%
Topics
People
- Ali Mostafa Anwar author maintainer
- Eleanor Coffey author ths
- Rasmus Hindström contributor
- Akewak Jeba author contributor
- Leo Lahti author ths