scales of CWT, by default it is the colnames of wCoefs
SNR.Th
threshold of SNR
peakScaleRange
the CWT scale range of the peak.
ridgeLength
the maximum ridge scale of the major peaks.
nearbyPeak
determine whether to include the small peaks close to large major peaks
nearbyWinSize
the window size to determine the nearby peaks. Only effective when nearbyPeak is true.
winSize.noise
the local window size to estimate the noise level.
SNR.method
method to estimate noise level. Currently, only 95 percentage quantile is supported.
minNoiseLevel
the minimum noise level used in calculating SNR, i.e., if the estimated noise level is less than "minNoiseLevel", it will use "minNoiseLevel" instead. If the noise level is less than 0.5, it will be treated as the ratio to the maximum amplitude of the spectrum.
Details
The determination of the peaks is based on three rules:
Rule 1: The maximum ridge scale of the peak should larger than a certain threshold
Rule 2: Based on the scale of the peak (corresponding to the maximum value of the peak ridge) should be within certain range
Rule 3: Based on the peak SNR
Value
Return a list with following elements:
peakIndex
the m/z indexes of the identified peaks
peakCenterIndex
the m/z indexes of peak centers, which correspond to the maximum on the ridge. peakCenterIndex includes all the peaks, not just the identified major peaks.
peakCenterValue
the CWT coefficients (the maximum on the ridge) corresponding to peakCenterIndex
peakSNR
the SNR of the peak, which is the ratio of peakCenterValue and noise level
peakScale
the estimated scale of the peak, which corresponds to the peakCenerIndex
potentialPeakIndex
the m/z indexes of all potential peaks, which satisfy all requirements of a peak without considering its SNR. Useful, if you want to change to a lower SNR threshold later.
allPeakIndex
the m/z indexes of all the peaks, whose order is the same as peakCenterIndex, peakCenterValue, peakSNR and peakScale.
All of these return elements have peak names, which are the same as the corresponding peak ridges. see getRidge for details.
Author(s)
Pan Du, Simon Lin
References
Du, P., Kibbe, W.A. and Lin, S.M. (2006) Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching, Bioinformatics, 22, 2059-2065.
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> library(MassSpecWavelet)
Loading required package: waveslim
waveslim: Wavelet Method for 1/2/3D Signals (version = 1.7.5)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/MassSpecWavelet/identifyMajorPeaks.Rd_%03d_medium.png", width=480, height=480)
> ### Name: identifyMajorPeaks
> ### Title: Identify peaks based on the ridges in 2-D CWT coefficient matrix
> ### Aliases: identifyMajorPeaks
> ### Keywords: methods
>
> ### ** Examples
>
> data(exampleMS)
> scales <- seq(1, 64, 3)
> wCoefs <- cwt(exampleMS, scales=scales, wavelet='mexh')
>
> localMax <- getLocalMaximumCWT(wCoefs)
> ridgeList <- getRidge(localMax)
>
> SNR.Th <- 3
> majorPeakInfo <- identifyMajorPeaks(exampleMS, ridgeList, wCoefs, SNR.Th=SNR.Th)
> ## Plot the identified peaks
> peakIndex <- majorPeakInfo$peakIndex
> plotPeak(exampleMS, peakIndex, main=paste('Identified peaks with SNR >', SNR.Th))
>
>
>
>
>
>
> dev.off()
null device
1
>