findPeaks.centWave-methods {xcms} | R Documentation |
Peak density and wavelet based feature detection for centroided LC/MS data
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 |
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.
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) |
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)
Ralf Tautenhahn, rtautenh@ipb-halle.de
findPeaks-methods
xcmsRaw-class