emzed.peak_picking.run_feature_finder_centwave module

emzed.peak_picking.run_feature_finder_centwave.install_xcms()[source]
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’