peakDetectionCWT {MassSpecWavelet} | R Documentation

## The main function of peak detection by CWT based pattern matching

### Description

This function is a wrapper of `cwt`, `getLocalMaximumCWT`, `getRidge`, `identifyMajorPeaks`

### Usage

    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, ...)

### Arguments

`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`

### Value

`majorPeakInfo` | return of `identifyMajorPeaks`
`ridgeList` | return of `getRidge`
`localMax` | return of `getLocalMaximumCWT`
`wCoefs` |

{ 2-D CWT coefficient matrix, see `cwt` 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.

### See Also

`cwt`, `getLocalMaximumCWT`, `getRidge`, `identifyMajorPeaks`

### Examples

            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))

[Package MassSpecWavelet version 1.6.0 Index]