Feature detection for high resolution LC/MS data

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

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, ROI.list=list()), firstBaselineCheck=TRUE, roiScales=NULL

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.

Arguments

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.
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
ROI.list: A optional list of ROIs that represents detected mass traces (ROIs). If this list is empty (default) then centWave detects the mass trace ROIs, otherwise this step is skipped and the supplied ROIs are used in the peak detection phase. Each ROI object in the list has the following slots: scmin start scan index, scmax end scan index, mzmin minimum m/z, mzmax maximum m/z, length number of scans, intensity summed intensity.
firstBaselineCheck: logical, if TRUE continuous data within ROI is checked to be above 1st baseline
roiScales: numeric, optional vector of scales for each ROI in ROI.list to be used for the centWave-wavelets

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)

Author

Ralf Tautenhahn

References

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