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MSclassifR (version 0.4.0)

Automated Classification of Mass Spectra

Description

Functions to classify mass spectra in known categories, and to determine discriminant mass-to-charge values. It includes easy-to-use functions for preprocessing 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 (). For a comprehensive guide, additional applications, and detailed examples of using this package, please visit our GitHub repository ().

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Install

install.packages('MSclassifR')

Monthly Downloads

422

Version

0.4.0

License

GPL (>= 3)

Issues

0

Pull Requests

0

Stars

2

Forks

1

Maintainer

Alexandre Godmer

Last Published

July 2nd, 2025

Functions in MSclassifR (0.4.0)

calculate_distance

Function calculating the distance between two vectors.
SignalProcessing

Function performing post acquisition signal processing
MSclassifR

Automated classification of mass spectra
smote_classif

SMOTE for Classification Problems
fast_generate_synthetic

Function generating synthetic examples using SMOTE
CitrobacterRKIspectra

Mass spectra corresponding to the bacterial species Citrobacter sp. from The Robert Koch-Institute (RKI) database of microbial MALDI-TOF mass spectra
PlotSpectra

Plot mass spectra with detected peaks
SelectionVarStat

Variable selection using multiple statistical tests.
LogReg

Estimation of a multinomial regression to predict the category to which a mass spectrum belongs
PeakDetection

Detection of peaks in MassSpectrum objects.
CitrobacterRKImetadata

Metadata of mass spectra corresponding to the bacterial species Citrobacter sp. from The Robert Koch-Institute (RKI) database of microbial MALDI-TOF mass spectra
PredictFastClass

Prediction of the category to which a mass spectrum belongs using linear regressions of mass spectra.
SelectionVar

Variable selection using random forests, logistic regression methods or sparse partial least squares discriminant analysis (sPLS-DA).
fast_find_neighbors

Function finding k Nearest Neighbors for each row of a matrix
d_left_join

Function joining two tables based not on exact matches
PredictLogReg

Prediction of the category to which a mass spectrum belongs from a multinomial logistic regression model