findPeaks.centWave-methods {xcms} | R Documentation

## Feature detection for high resolution LC/MS data

### Description

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

### 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
`snthresh` | signal to noise ratio cutoff, definition see below.
`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`.
`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

### 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 mass traces (characterised as regions with less than `ppm` m/z deviation in consecutive scans) in the LC/MS map are located. In the second phase these mass traces are further analysed. Continuous wavelet transform (CWT) is used to locate chromatographic peaks on different scales.

### 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)

### Methods

object = "xcmsRaw"
  `findPeaks.centWave(object, ppm=25, peakwidth=c(20,50), snthresh=10, prefilter=c(3,100), integrate=1, mzdiff=-0.001, fitgauss=FALSE, scanrange= numeric(), noise=0, sleep=0, verbose.columns=FALSE)`

### Author(s)

Ralf Tautenhahn, rtautenh@ipb-halle.de

### See Also

`findPeaks-methods` `xcmsRaw-class`

[Package xcms version 1.16.3 Index]