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MSclassifR

https://github.com/agodmer/msclassifr_examples

Science Score: 39.0%

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Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: agodmer
  • License: gpl-3.0
  • Language: HTML
  • Default Branch: main
  • Size: 4.68 MB
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Created over 4 years ago · Last pushed about 1 year ago
Metadata Files
Readme License

README.md

MSclassifR: an R Package for Supervised Classification of Mass Spectra with Machine Learning Methods

Total Downloads Weekly Downloads Version CRAN Checks GPL v3 License DOI Badge

1. Description

This package provides R functions to classify mass spectra in known categories, and to determine discriminant mass-to-charge values. It was developed with the aim of identifying very similar species or phenotypes of bacteria from mass spectra obtained by Matrix Assisted Laser Desorption Ionisation - Time Of Flight Mass Spectrometry (MALDI-TOF MS). However, the different functions of this package can also be used to classify other categories associated to mass spectra; or from mass spectra obtained with other mass spectrometry techniques. It includes easy-to-use functions for pre-processing mass spectra, functions to determine discriminant mass-to-charge values (m/z) from a library of mass spectra corresponding to different categories, and functions to predict the category (species, phenotypes, etc.) associated to a mass spectrum from a list of selected mass-to-charge values. If you use this package in your research, please cite the associated publication available here.

2. Installation

The installation of the MSclassifR package requires the installation of packages from Bioconductor, so you might have to install the latest version of the BiocManager package. The MSclassifR package imports the other necessary packages from the CRAN. In addition, it is recommended to install the latest version of R.

```

install BiocManager if not installed

if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager")

Install the mixOmics and multtest packages from Bioconductor

BiocManager::install(c("multtest","mixOmics", "limma", "qvalue", "cp4p"))

Install MSclassifR package

install.packages("MSclassifR")

Check after install the MSclassifR package:

require(MSclassifR) ## For spectral easy signal processing and machine learning ```

3. Your Guide to MSclassifR: step-by-step examples and case studies

Users typically begin by importing their MALDI-TOF mass spectra into R using the function "MALDIquantForeign::importBrukerFlex(YourPathway)". They then create a dataframe that categorizes each mass spectrum by strain and/or species (see the 'Ecrobia' and 'Klebsiella' vignettes for examples). These categorical assignments are generally determined through Whole Genome Sequencing (WGS) or by evaluating phenotypic antimicrobial sensitivity profiles, depending on the problematic.

Once users have prepared a dataset with these categorical assignments, they can train classification models using their dataset. These models can then be saved and subsequently used to predict categories for new MALDI-TOF mass spectra (avoiding the use of WGS or phenotypic antimicrobial sensitivity profiles).

  • Delve deeper into MSclassifR: article

  • Code and reproducibility resources:

    • codes used for the numerical experiments related to the MSclassifR article are publicly available and versioned at this link. Users can notably adapt the gs2() and eG() functions of the Run_experiments.R to their problematic.
    • the computing environment is specified using a sessionInfo() output to ensure reproducibility.
    • input datasets, when not proprietary, are included or clearly referenced in the repository.

  • If you use MSclassifR or any of the code/workflows from this repository, please cite the following article:

Godmer, A., Benzerara, Y., Varon, E., Veziris, N., Druart, K., Mozet, R., Matondo, M., Aubry, A., & Giai Gianetto, Q. (2025). MSclassifR: An R package for supervised classification of mass spectra with machine learning methods. Expert Systems with Applications, 294, 128796. https://doi.org/10.1016/j.eswa.2025.128796

🗨️ Questions or suggestions? We welcome your feedback! Join the discussion on our forum.

4. Practical workflow using MSclassifR

MSclassifR Workflow

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  • Login: agodmer
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 352 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 7
  • Total maintainers: 1
cran.r-project.org: MSclassifR

Automated Classification of Mass Spectra

  • Versions: 7
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 352 Last month
Rankings
Forks count: 28.8%
Dependent packages count: 29.8%
Stargazers count: 35.2%
Dependent repos count: 35.5%
Average: 37.1%
Downloads: 56.4%
Maintainers (1)
alexandre.godmer@aphp.fr
Last synced: 11 months ago