PeakList {ptairMS} | R Documentation

## 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

[Package ptairMS version 1.2.0 Index]