findPeaks.centWave-methods        package:xcms         R Documentation

Feature detection for high resolution LC/MS data

Description:

     Peak density and wavelet based feature detection for high
     resolution LC/MS data in centroid mode

Arguments:

  object: 'xcmsSet' object

     ppm: maximal tolerated m/z deviation in consecutive scans, in ppm
          (parts per million)

peakwidth: Chromatographic peak width, given as range (min,max) in
          seconds

snthresh: signal to noise ratio cutoff, definition see below.

prefilter: 'prefilter=c(k,I)'. Prefilter step for the first phase. Mass
          traces are only retained if they contain at least 'k' peaks
          with intensity >= 'I'.

mzCenterFun: Function to calculate the m/z center of the feature:
          'wMean' intensity weighted mean of the feature m/z values,
          'mean' mean of the feature m/z values, 'apex' use m/z value
          at peak apex, 'wMeanApex3' intensity weighted mean of the m/z
          value at peak apex and the m/z value left and right of it,
          'meanApex3' mean of the m/z value at peak apex and the m/z
          value left and right of it.

integrate: Integration method. If '=1' peak limits are found through
          descent on the mexican hat filtered data, if '=2' the descent
          is done on the real data. Method 2 is very accurate but prone
          to noise, while method 1 is more robust to noise but less
          exact.

  mzdiff: minimum difference in m/z for peaks with overlapping
          retention times, can be negative to allow overlap

fitgauss: logical, if TRUE a Gaussian is fitted to each peak

scanrange: scan range to process

   noise: optional argument which is useful for data that was
          centroided without any intensity threshold, centroids with
          intensity < 'noise' are omitted from ROI detection

   sleep: number of seconds to pause between plotting peak finding
          cycles

verbose.columns: logical, if TRUE additional peak meta data columns are
          returned

Details:

     This algorithm is most suitable for high resolution
     LC/{TOF,OrbiTrap,FTICR}-MS data in centroid mode. In the first
     phase of the method mass traces (characterised as regions with
     less than 'ppm' m/z deviation in consecutive scans) in the LC/MS
     map are located.  In the second phase these mass traces are
     further analysed.  Continuous wavelet transform (CWT) is used to
     locate chromatographic peaks on different scales.

Value:

     A matrix with columns:

      mz: weighted (by intensity) mean of peak m/z across scans

   mzmin: m/z peak minimum

   mzmax: m/z peak maximum

      rt: retention time of peak midpoint

   rtmin: leading edge of peak retention time

   rtmax: trailing edge of peak retention time

    into: integrated peak intensity

    intb: baseline corrected integrated peak intensity

    maxo: maximum peak intensity

      sn: Signal/Noise ratio, defined as '(maxo - baseline)/sd', where
          'maxo' is the maximum peak intensity,
          'baseline' the estimated baseline value and
          'sd' the standard deviation of local chromatographic noise.

  egauss: RMSE of Gaussian fit

        : if 'verbose.columns' is 'TRUE' additionally :

      mu: Gaussian parameter mu

   sigma: Gaussian parameter sigma

       h: Gaussian parameter h

       f: Region number of m/z ROI where the peak was localised

    dppm: m/z deviation of mass trace across scans in ppm

   scale: Scale on which the peak was localised

   scpos: Peak position found by wavelet analysis

   scmin: Left peak limit found by wavelet analysis (scan number)

   scmax: Right peak limit found by wavelet analysis (scan number)

Methods:

     object = "xcmsRaw" ' findPeaks.centWave(object, ppm=25,
          peakwidth=c(20,50), snthresh=10, prefilter=c(3,100),
          mzCenterFun="wMean", integrate=1, mzdiff=-0.001,
          fitgauss=FALSE, scanrange= numeric(), noise=0, sleep=0,
          verbose.columns=FALSE) '

Author(s):

     Ralf Tautenhahn

References:

     Ralf Tautenhahn, Christoph Boettcher, and Steffen Neumann "Highly
     sensitive feature detection for high resolution LC/MS" BMC
     Bioinformatics 2008, 9:504

See Also:

     'findPeaks-methods' 'xcmsRaw-class'


