PRONE
Bioc currentThe PROteomics Normalization Evaluator
Release Lineage
Entered 3.20 · Oct 30, 2024
Current · Requires R 4.6
Description
High-throughput omics data are often affected by systematic biases introduced throughout all the steps of a clinical study, from sample collection to quantification. Normalization methods aim to adjust for these biases to make the actual biological signal more prominent. However, selecting an appropriate normalization method is challenging due to the wide range of available approaches. Therefore, a comparative evaluation of unnormalized and normalized data is essential in identifying an appropriate normalization strategy for a specific data set. This R package provides different functions for preprocessing, normalizing, and evaluating different normalization approaches. Furthermore, normalization methods can be evaluated on downstream steps, such as differential expression analysis and statistical enrichment analysis. Spike-in data sets with known ground truth and real-world data sets of biological experiments acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed.
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
112 90 exported
Complexity
5 avg / 22 max
Call network
112 nodes / 92 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
11,555
Files
153
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
90
Internal functions
22
Recent export changes
Testing
Has tests
No
Test-to-code ratio
0.00
testthat edition
3
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
0%
Unsafe pattern score
15
Dep constraint coverage
0%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
4.4.0
System requirements
–
C++ standard
–
License
GPL (>= 3)
License flags
SPDX valid, OSI approved
History
Versions
4
First release
2025-03-21
Latest release
2026-04-28
Avg cadence
168 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
- 106 (14 internal)includes a package overview page
- Examples that run
- 100%80 run in a plain check
- Exported functions with examples
- 89%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Repository practices
3 development-tooling and community-health practices detected across 1 family in the upstream repository
Checks run against github.com/daisybio/prone on 2026-10-18.
Datasets
| Name | Class | Size | Also in |
|---|---|---|---|
| Ecoli_human_MaxLFQ_metadata | data.frame | 6 × 6 | No other package |
| Ecoli_human_MaxLFQ_protein_intensities | data.frame | 1,000 × 11 | No other package |
| mouse_liver_cytochrome_P450_metadata | data.frame | 12 × 3 | No other package |
| mouse_liver_cytochrome_P450_protein_intensities | data.frame | 1,499 × 15 | No other package |
| spike_in_de_res | data.table | 7,500 × 10 | No other package |
| spike_in_se | – | – | – |
| tuberculosis_TMT_de_res | data.table | 9,030 × 9 | No other package |
| tuberculosis_TMT_se | – | – | – |
| tuberculosis_metadata | data.frame | 20 × 5 | No other package |
| tuberculosis_protein_intensities | data.frame | 301 × 140 | No other package |
Topics
People
- Lis Arend author maintainer
Cite
Cite this package
Run in R for the authors' entry:
citation("PRONE")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1093/bib/bbaf201.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.