emzed.peak_picking.run_feature_finder_centwave module¶
- emzed.peak_picking.run_feature_finder_centwave.run_feature_finder_centwave(peakmap, ppm=25, peakwidth=(20, 50), prefilter=(3, 100), snthresh=10, integrate=1, mzdiff=-0.001, noise=0, mzCenterFun='wMean', fitgauss=False, msLevel=None, verboseColumns=False, roiList=[], firstBaselineCheck=False, roiScales=[], extendLengthMSW=False, verboseBetaColumns=False)[source]¶
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’