peakDetectionCWT {MassSpecWavelet} | R Documentation |
This function is a wrapper of cwt
, getLocalMaximumCWT
, getRidge
, identifyMajorPeaks
peakDetectionCWT(ms, scales = c(1, seq(2, 30, 2), seq(32, 64, 4)), SNR.Th = 3, nearbyPeak = TRUE, peakScaleRange = 5, amp.Th = 0.01, minNoiseLevel = amp.Th/SNR.Th, ridgeLength = 24, tuneIn = FALSE, ...)
ms |
the mass spectrometry spectrum |
scales |
scales of CWT |
SNR.Th |
SNR (Signal to Noise Ratio) threshold |
nearbyPeak |
Determine whether to include the nearby small peaks of major peaks. TRUE by default |
peakScaleRange |
the scale range of the peak. larger than 5 by default. |
amp.Th |
the minimum required amplitude of the peak |
minNoiseLevel |
the minimum noise level used in computing the SNR |
ridgeLength |
the minimum highest scale of the peak in 2-D CWT coefficient matrix |
tuneIn |
determine whether to tune in the parameter estimation of the detected peaks |
... |
other parameters used by identifyMajorPeaks |
majorPeakInfo |
return of identifyMajorPeaks |
ridgeList |
return of getRidge |
localMax |
return of getLocalMaximumCWT |
wCoefs |
{ 2-D CWT coefficient matrix, see cwt
for details.}
Pan Du, Simon Lin
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.
cwt
, getLocalMaximumCWT
, getRidge
, identifyMajorPeaks
data(exampleMS) SNR.Th <- 3 peakInfo <- peakDetectionCWT(exampleMS, SNR.Th=SNR.Th) majorPeakInfo = peakInfo$majorPeakInfo peakIndex <- majorPeakInfo$peakIndex plotPeak(exampleMS, peakIndex, main=paste('Identified peaks with SNR >', SNR.Th))