Feature detection based on predicted isotope features for high resolution LC/MS data

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

Methods

object = "xcmsRaw": findPeaks.centWave(object, ppm=25, peakwidth=c(20,50), prefilter=c(3,100), mzCenterFun="wMean", integrate=1, mzdiff=-0.001, fitgauss=FALSE, scanrange= numeric(), noise=0, sleep=0, verbose.columns=FALSE, xcmsPeaks, snthresh=6.25, maxcharge=3, maxiso=5, mzIntervalExtension=TRUE)

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 isotope ROIs (regions of interest) in the LC/MS map are predicted. In the second phase these mass traces are further analysed. Continuous wavelet transform (CWT) is used to locate chromatographic peaks on different scales. The resulting peak list and the given peak list (xcmsPeaks) are merged and redundant peaks are removed.

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
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
xcmsPeaks: peak list picked using the centWave algorithm with parameter verbose.columns set to TRUE (columns scmin and scmax needed)
snthresh: signal to noise ratio cutoff, definition see below.
maxcharge: max. number of the isotope charge.
maxiso: max. number of the isotope peaks to predict for each detected feature.
mzIntervalExtension: logical, if TRUE predicted isotope ROIs (regions of interest) are extended in the m/z dimension to increase the detection of low intensity and hence noisy peaks.

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\ Hendrik Treutler and Steffen Neumann. "Prediction, detection, and validation of isotope clusters in mass spectrometry data" Submitted to Metabolites 2016, Special Issue "Bioinformatics and Data Analysis"