pmp
Bioc currentPeak Matrix Processing and signal batch correction for metabolomics datasets
Release Lineage
Entered 3.11 · Apr 28, 2020
Current · Requires R 4.6
Description
Methods and tools for (pre-)processing of metabolomics datasets (i.e. peak matrices), including filtering, normalisation, missing value imputation, scaling, and signal drift and batch effect correction methods. Filtering methods are based on: the fraction of missing values (across samples or features); Relative Standard Deviation (RSD) calculated from the Quality Control (QC) samples; the blank samples. Normalisation methods include Probabilistic Quotient Normalisation (PQN) and normalisation to total signal intensity. A unified user interface for several commonly used missing value imputation algorithms is also provided. Supported methods are: k-nearest neighbours (knn), random forests (rf), Bayesian PCA missing value estimator (bpca), mean or median value of the given feature and a constant small value. The generalised logarithm (glog) transformation algorithm is available to stabilise the variance across low and high intensity mass spectral features. Finally, this package provides an implementation of the Quality Control-Robust Spline Correction (QCRSC) algorithm for signal drift and batch effect correction of mass spectrometry-based datasets.
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
28 13 exported
Complexity
3.4 avg / 11 max
Call network
28 nodes / 43 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
5,020
Files
44
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
13
Internal functions
15
Testing
Has tests
Yes
Test-to-code ratio
0.35
testthat edition
–
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
4.0
System requirements
–
C++ standard
–
License
GPL-3
License flags
SPDX valid, OSI approved
History
Versions
13
First release
2020-04-27
Latest release
2026-04-28
Avg cadence
179 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
- 14
- Examples that run
- 100%13 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 29%
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 |
|---|---|---|---|
| MTBLS79 | – | – | – |
1 internal object is bundled for the package's own use and not listed here.
Topics
Suggested by (2)
People
- Gavin Rhys Lloyd author maintainer
- Andris Jankevics author
- Ralf Johannes Maria Weber author
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("pmp")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
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