PeakList: Detection and quantification of peaks on a sum spectrum.

In camilleroquencourt/ptairMS: Pre-processing PTR-TOF-MS Data

Detection and quantification of peaks on a sum spectrum.

Description

Detection and quantification of peaks on a sum spectrum.

Usage

PeakList(
  raw,
  mzNominal = unique(round(getRawInfo(raw)$mz)),
  ppm = 130,
  resolutionRange = c(3000, 5000, 8000),
  minIntensity = 5,
  fctFit = c("sech2", "averagePeak")[1],
  peakShape = NULL,
  maxIter = 1,
  R2min = 0.995,
  autocorNoiseMax = 0.3,
  plotFinal = FALSE,
  plotAll = FALSE,
  thNoiseRate = 1.1,
  minIntensityRate = 0.01,
  countFacFWHM = 10,
  daSeparation = 0.005,
  d = 3,
  windowSize = 0.4
)

## S4 method for signature 'ptrRaw'
PeakList(
  raw,
  mzNominal = unique(round(getRawInfo(raw)$mz)),
  ppm = 130,
  resolutionRange = c(300, 5000, 8000),
  minIntensity = 5,
  fctFit = c("sech2", "averagePeak")[1],
  peakShape = NULL,
  maxIter = 3,
  R2min = 0.995,
  autocorNoiseMax = 0.3,
  plotFinal = FALSE,
  plotAll = FALSE,
  thNoiseRate = 1.1,
  minIntensityRate = 0.01,
  countFacFWHM = 10,
  daSeparation = 0.005,
  d = 3,
  windowSize = 0.4
)

Arguments

raw	ptrRaw-class object
mzNominal	the vector of nominal mass where peaks will be detected
ppm	the minimum distance between two peeks in ppm
resolutionRange	vector with resolution min, resolution Mean, and resolution max of the PTR
minIntensity	the minimum intenisty for peaks detection. The final threshold for peak detection will be : max ( minPeakDetect , thresholdNoise ). The thresholdNoise correspond to max(thNoiseRate * max( noise around the nominal mass), minIntensityRate * max( intenisty in the nominal mass). The noise around the nominal mass correspond : [m-windowSize-0.2,m-windowSize]U[m+windowSize,m+WindowSize+0.2].
fctFit	the function for the quantification of Peak, should be average or Sech2
peakShape	a list with reference axis and a reference peak shape centered in zero
maxIter	maximum iteration of residual analysis
R2min	R2 minimum to stop the iterative residual analysis
autocorNoiseMax	the autocorelation threshold for Optimal windows Savitzky Golay filter in OptimalWindowSG ptairMS function. See ?OptimalWindowSG
plotFinal	boolean. If TRUE, plot the spectrum for all nominal masses, with the final fitted peaks
plotAll	boolean. Tf TRUE, plot all step to get the final fitted peaks
thNoiseRate	The rate which multiplies the max noise intensity
minIntensityRate	The rate which multiplies the max signal intensity
countFacFWHM	integer. We will sum the fitted peaks on a window's size of countFacFWHM * FWHM, centered in the mass peak center.
daSeparation	the minimum distance between two peeks in Da for nominal mass < 17.
d	the degree for the Savitzky Golay filtrer
windowSize	peaks will be detected only around m - windowSize ; m + windowSize, for all m in mzNominal

Value

a list containing:

• peak: a data.frame, with for all peak detected: the mass center, the intensity count in cps, the peak width (delta_mz), correspond to the Full Width Half Maximum (FWHM),the resolution m/delta_m, the other parameters values estimated of fitFunc.

• warnings: warnings generated by the peak detection algorithm per nominal masses

• infoPlot: elements needed to plot the fitted peak per nominal masses

Examples

library(ptairData)
filePath <- system.file('extdata/exhaledAir/ind1', 'ind1-1.h5', 
package = 'ptairData')
file <- readRaw(filePath)

peakList <- PeakList(file, mzNominal = c(21,63))
peakList$peak

camilleroquencourt/ptairMS documentation built on June 9, 2024, 10:35 a.m.