findPeaks.centWave-methods {xcms}R Documentation

Feature detection for centroided LC/MS data

Description

Peak density and wavelet based feature detection for centroided LC/MS data

Arguments

object xcmsSet object
scanrange scan range to check for peaks
minEntries minimum number of peaks(centroids) within the range of dev ppm
dev see above
snthresh signal to noise ratio cutoff
noiserange neighbourhood size (number of scans) for each ROI used for noise/baseline analysis, two region sizes can be used for better local basline estimation
minPeakWidth Minimum extent (number of scans) of a peak. Only used to estimate other parameters.
scales scales to use for wavelet analysis, the scale range should correspond to the width range of the chromatographic peaks (number of scans)
maxGaussOverlap maximal allowed overlapping (peak area)
minPtsAboveBaseLine minimum number of data points above baseline
scRangeTol tolerance (in scans) if gaussian peak center is not within the initial ROI
maxDescOutlier Maximum number of outliers on the descent to find peak borders (used if integrate=2)
mzdiff minimum difference in m/z for peaks with overlapping retention times, can be negative to allow overlap
rtdiff minimum difference in retention time for peaks with overlapping m/z, can be negative to allow overlap
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 honest, while method 1 is more robust to noise.
sleep number of seconds to pause between plotting peak finding cycles
fitgauss logical, if TRUE a gaussian is fitted to each peak
verbose.columns logical, if TRUE additional peak meta data columns are returned

Details

This algorithm is most suitable for high resolution centroid LC/TOF-MS data. In the first phase of the method areas of high peak density (characterised by having at least minEntries peaks within dev ppm in consecutive scans) in the LC/MS map are located. In the second phase these regions of interest (ROI) 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 of minimum step
mzmax m/z of maximum step
rt retention time of peak midpoint
rtmin leading edge of peak retention time
rtmax trailing edge of peak retention time
into integrated area of original (raw) peak
maxo maximum intensity of original (raw) peak
sn Signal/Noise ratio, calculated as (maxo / baseline value)
egauss RMSE of Gaussian fit
if verbose.columns is TRUE additionally :
mu Gauss parameter $μ$
sigma Gauss parameter $σ$
h Gauss parameter h
f ROI number
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)
lmin leading edge of peak (scan number)
lmax trailing edge of peak (scan number)

Methods

object = "xcmsRaw"
findPeaks.centWave(object, scanrange=c(1,length(object@scantime)), minEntries=4, dev=140e-6, snthresh=20, minPeakWidth=7, noiserange=c(minPeakWidth*3,minPeakWidth*6), scales=c(5,7,9,12,16,20), maxGaussOverlap = 0.5, minPtsAboveBaseLine=4, scRangeTol=2, maxDescOutlier=floor(minPeakWidth/2), mzdiff=-0.001, rtdiff=-round(2/3 *minPeakWidth *mean(diff(object@scantime))), integrate=1, sleep=0, fitgauss = FALSE, verbose.columns = FALSE)

Author(s)

Ralf Tautenhahn, rtautenh@ipb-halle.de

See Also

findPeaks-methods xcmsRaw-class


[Package xcms version 1.10.7 Index]