findPeaks.centWave-methods       package:xcms       R Documentation

_F_e_a_t_u_r_e _d_e_t_e_c_t_i_o_n _f_o_r _h_i_g_h _r_e_s_o_l_u_t_i_o_n _L_C/_M_S _d_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

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

_A_r_g_u_m_e_n_t_s:

  object: 'xcmsSet' object

     ppm: maxmial 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'.

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  

_D_e_t_a_i_l_s:

     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.

_V_a_l_u_e:

     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)

_M_e_t_h_o_d_s:


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


_A_u_t_h_o_r(_s):

     Ralf Tautenhahn, rtautenh@ipb-halle.de

_S_e_e _A_l_s_o:

     'findPeaks-methods' 'xcmsRaw-class'

