MetNet
Bioc currentInferring metabolic networks from untargeted high-resolution mass spectrometry data
Release Lineage
Entered 3.8 · Oct 31, 2018
Current · Requires R 4.6
Description
MetNet contains functionality to infer metabolic network topologies from quantitative data and high-resolution mass/charge information. Using statistical models (including correlation, mutual information, regression and Bayes statistics) and quantitative data (intensity values of features) adjacency matrices are inferred that can be combined to a consensus matrix. Mass differences calculated between mass/charge values of features will be matched against a data frame of supplied mass/charge differences referring to transformations of enzymatic activities. In a third step, the two levels of information are combined to form a adjacency matrix inferred from both quantitative and structure information.
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
21 17 exported
Complexity
6.3 avg / 21 max
Call network
21 nodes / 8 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
7,626
Files
64
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
17
Internal functions
4
Recent export changes
Testing
Has tests
Yes
Test-to-code ratio
0.59
testthat edition
–
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
–
Unsafe pattern score
3
Dep constraint coverage
100%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
4.1
System requirements
–
C++ standard
–
License
GPL (>= 3)
License flags
SPDX valid, OSI approved
History
Versions
16
First release
2019-01-04
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
100%
Dep drift
18
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Help pages
- 32includes a package overview page
- Examples that run
- 100%20 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 98%
- Return-value docs
- 100%
- Help pages citing references
- 16%
Repository practices
Checks read the Bioconductor git repository (git.bioconductor.org), branch RELEASE_3_23.
2 practices detected in the Bioconductor git repository
Datasets
| Name | Class | Size | Also in |
|---|---|---|---|
| mat_test | matrix | 7 × 20 | No other package |
| mat_test_z | matrix | 20 × 7 | No other package |
| peaklist | data.frame | 5,067 × 122 | No other package |
| sps_sub | – | – | – |
| x_annotation | data.frame | 36 × 11 | No other package |
| x_test | data.frame | 36 × 122 | No other package |
Topics
People
- Thomas Naake author maintainer
- Elva Maria Novoa-del-Toro contributor
- Liesa Salzer contributor
Cite
Cite this package
Run in R for the authors' entry:
citation("MetNet")The package provides a citation file, so citation() returns the authors' own entry.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.