MSclassifR (version 0.4.0)

PeakDetection: Detection of peaks in MassSpectrum objects.

Description

This function performs a data analysis pipeline to pre-process mass spectra. It provides average intensities and detects peaks using functions of R packages MALDIquant and MALDIrppa.

Usage

PeakDetection(x,
              averageMassSpec = TRUE,
              labels = NULL,
              averageMassSpectraMethod = "median",
              SNRdetection = 3,
              binPeaks = TRUE,
              PeakDetectionMethod = "MAD",
              halfWindowSizeDetection = 11,
              AlignMethod = "strict",
              Tolerance = 0.002,
              ...)

Value

Returns a list of MassPeaks objects (see MALDIquant R package) for each mass spectrum in x.

Arguments

x

a list of MassSpectrum objects (see MALDIquant R package).

averageMassSpec

a logical value indicating whether it is necessary to group the spectra according to the arguments labels and averageMassSpectraMethod. It is fixed to "TRUE" by default.

labels

a list of factor objects to do groupwise averaging.

averageMassSpectraMethod

a character indicating the method used to average mass spectra according to labels. It is fixed to "median" by default.This function can be replaced by another mathematical function such as "mean". See averageMassSpectra of MALDIquant R package.

PeakDetectionMethod

a character indicating the noise estimation method. It uses "MAD" method for list of MassSpectrum objects. This noise estimation method estimation method can be remplaced "SuperSmoother". See estimateNoise-methods of the MALDIquant R package for details.

SNRdetection

a numeric value indicating the signal-to-noise ratio used to detect peaks (by default = 3). See detectPeaks-methods of the MALDIquant R package for details.

binPeaks

a logical value indicating the peaks are aligned in discrete bins. It is fixed to "TRUE" by default. See binPeaks of the MALDIquant R package for details.

halfWindowSizeDetection

a numeric value half window size to detect peaks (by default = 11). See detectPeaks-methods of the MALDIquant R package for details.

AlignMethod

a character indicating the method used to equalize masses for similar peaks. The "strict" method is used by default corresponding to a unique peak per bin from the same sample. This method can be remplaced by "relaxed" corresponding to multiple peaks per bin from the same sample. See binPeaks of the MALDIquant R package for more details.

Tolerance

a numeric value corresponding to the maximal deviation in peak masses to be considered as identical in ppm (by default = 0.002). See determineWarpingFunctions of the MALDIquant R package for details.

...

other arguments from MALDIquant and MALDIrppa packages.

Details

The PeakDetection function provides an analysis pipeline for MassSpectrum objects including peaks detection and binning.

All the methods used for PeakDetection functions are selected from MALDIquant and MALDIrppa packages.

References

Gibb S, Strimmer K. MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics. 2012 Sep 1;28(17):2270-1. tools:::Rd_expr_doi("10.1093/bioinformatics/bts447"). Epub 2012 Jul 12. PMID: 22796955.

Javier Palarea-Albaladejo, Kevin Mclean, Frank Wright, David G E Smith, MALDIrppa: quality control and robust analysis for mass spectrometry data, Bioinformatics, Volume 34, Issue 3, 01 February 2018, Pages 522 - 523, tools:::Rd_expr_doi("https://doi.org/10.1093/bioinformatics/btx628")

Alexandre Godmer, Yahia Benzerara, Emmanuelle Varon, Nicolas Veziris, Karen Druart, Renaud Mozet, Mariette Matondo, Alexandra Aubry, Quentin Giai Gianetto, MSclassifR: An R package for supervised classification of mass spectra with machine learning methods, Expert Systems with Applications, Volume 294, 2025, 128796, ISSN 0957-4174, tools:::Rd_expr_doi("10.1016/j.eswa.2025.128796").

Examples

library("MALDIquant")
library("MSclassifR")


## Load mass spectra and metadata
data("CitrobacterRKIspectra", "CitrobacterRKImetadata", package = "MSclassifR")

## Pre-processing of mass spectra
spectra <- SignalProcessing(CitrobacterRKIspectra)

## Detection of peaks in pre-processed mass spectra
peaks <- PeakDetection(x = spectra,
                       averageMassSpec = FALSE,
                       labels = CitrobacterRKImetadata$Strain_name_spot,
                       averageMassSpectraMethod = "median",
                       SNRdetection = 3,
                       binPeaks = TRUE,
                       halfWindowSizeDetection = 11,
                       AlignFrequency = 0.20,
                       AlignMethod = "strict",
                       Tolerance = 0.002)

# Plot peaks on a pre-processed mass spectrum
PlotSpectra(SpectralData=spectra[[1]],Peaks=peaks[[1]],col_spec="blue",col_peak="black")