do_findChromPeaks_centWave {xcms} | R Documentation

## Core API function for centWave peak detection

### Description

This function performs peak density and wavelet based chromatographic peak detection for high resolution LC/MS data in centroid mode [Tautenhahn 2008].

### Usage

    do_findChromPeaks_centWave(mz, int, scantime, valsPerSpect, ppm = 25,
      peakwidth = c(20, 50), snthresh = 10, prefilter = c(3, 100),
      mzCenterFun = "wMean", integrate = 1, mzdiff = -0.001,
      fitgauss = FALSE, noise = 0, verboseColumns = FALSE, roiList = list(),
      firstBaselineCheck = TRUE, roiScales = NULL, sleep = 0)

### Arguments

`mz` | Numeric vector with the individual m/z values from all scans/ spectra of one file/sample.
`int` | Numeric vector with the individual intensity values from all scans/spectra of one file/sample.
`scantime` | Numeric vector of length equal to the number of spectra/scans of the data representing the retention time of each scan.
`valsPerSpect` | Numeric vector with the number of values for each spectrum.
`ppm` | `numeric(1)` defining the maximal tolerated m/z deviation in consecutive scans in parts per million (ppm) for the initial ROI definition.
`peakwidth` | `numeric(2)` with the expected approximate peak width in chromatographic space. Given as a range (min, max) in seconds.
`snthresh` | `numeric(1)` defining the signal to noise ratio cutoff.
`prefilter` | `numeric(2)`: `c(k, I)` specifying the prefilter step for the first analysis step (ROI detection). Mass traces are only retained if they contain at least `k` peaks with intensity `>= I`.
`mzCenterFun` | Name of the function to calculate the m/z center of the chromatographic peak. Allowed are: `"wMean"`: intensity weighted mean of the peak's m/z values, `"mean"`: mean of the peak's m/z values, `"apex"`: use the m/z value at the peak apex, `"wMeanApex3"`: intensity weighted mean of the m/z value at the peak apex and the m/z values left and right of it and `"meanApex3"`: mean of the m/z value of the peak apex and the m/z values left and right of it.
`integrate` | Integration method. For `integrate = 1` peak limits are found through descent on the mexican hat filtered data, for `integrate = 2` the descent is done on the real data. The latter method is more accurate but prone to noise, while the former is more robust, but less exact.
`mzdiff` | `numeric(1)` representing the minimum difference in m/z dimension for peaks with overlapping retention times; can be negative to allow overlap.
`fitgauss` | `logical(1)` whether or not a Gaussian should be fitted to each peak.
`noise` | `numeric(1)` allowing to set a minimum intensity required for centroids to be considered in the first analysis step (centroids with intensity `< noise` are omitted from ROI detection).
`verboseColumns` | `logical(1)` whether additional peak meta data columns should be returned.
`roiList` | An optional list of regions-of-interest (ROI) representing detected mass traces. If ROIs are submitted the first analysis step is omitted and chromatographic peak detection is performed on the submitted ROIs. Each ROI is expected to have the following elements specified: `scmin` (start scan index), `scmax` (end scan index), `mzmin` (minimum m/z), `mzmax` (maximum m/z), `length` (number of scans), `intensity` (summed intensity). Each ROI should be represented by a `list` of elements or a single row `data.frame`.
`firstBaselineCheck` | `logical(1)`. If `TRUE` continuous data within regions of interest is checked to be above the first baseline.
`roiScales` | Optional numeric vector with length equal to `roiList` defining the scale for each region of interest in `roiList` that should be used for the centWave-wavelets.
`sleep` | `numeric(1)` defining the number of seconds to wait between iterations. Defaults to `sleep = 0`. If `> 0` a plot is generated visualizing the identified chromatographic peak. Note: this argument is for backward compatibility only and will be removed in future.

### Details

This algorithm is most suitable for high resolution LC/{TOF,OrbiTrap,FTICR}-MS data in centroid mode. In the first phase the method identifies regions of interest (ROIs) representing mass traces that are characterized as regions with less than `ppm` m/z deviation in consecutive scans in the LC/MS map. These ROIs are then subsequently analyzed using continuous wavelet transform (CWT) to locate chromatographic peaks on different scales. The first analysis step is skipped, if regions of interest are passed with the `roiList` parameter.

### Value

A matrix, each row representing an identified chromatographic peak, with columns:

mz

  Intensity weighted mean of m/z values of the peak across scans.

mzmin

  Minimum m/z of the peak.

mzmax

  Maximum m/z of the peak.

rt

  Retention time of the peak's midpoint.

rtmin

  Minimum retention time of the peak.

rtmax

  Maximum retention time of the peak.

into

  Integrated (original) intensity of the peak.

intb

  Per-peak baseline corrected integrated peak intensity.

maxo

  Maximum intensity of the peak.

sn

  Signal to noise ratio, defined as `(maxo - baseline)/sd`, `sd` being the standard deviation of local chromatographic noise.

egauss

  RMSE of Gaussian fit.

Additional columns for `verboseColumns = TRUE`:

mu

  Gaussian parameter mu.

sigma

  Gaussian parameter sigma.

h

  Gaussian parameter h.

f

  Region number of the m/z ROI where the peak was localized.

dppm

  m/z deviation of mass trace across scans in ppm.

scale

  Scale on which the peak was localized.

scpos

  Peak position found by wavelet analysis (scan number).

scmin

  Left peak limit found by wavelet analysis (scan number).

scmax

  Right peak limit found by wavelet analysis (scan numer).

### Note

The centWave was designed to work on centroided mode, thus it is expected that such data is presented to the function.

This function exposes core chromatographic peak detection functionality of the centWave method. While this function can be called directly, users will generally call the corresponding method for the data object instead.

### Author(s)

Ralf Tautenhahn, Johannes Rainer

### References

Ralf Tautenhahn, Christoph B\"ottcher, and Steffen Neumann "Highly sensitive feature detection for high resolution LC/MS" BMC Bioinformatics 2008, 9:504

### See Also

`centWave` for the standard user interface method.

Other core peak detection functions: `do_findChromPeaks_centWaveWithPredIsoROIs`, `do_findChromPeaks_massifquant`, `do_findChromPeaks_matchedFilter`, `do_findPeaks_MSW`

### Examples

    ## Load the test file
    library(faahKO)
    fs <- system.file('cdf/KO/ko15.CDF', package = "faahKO")
    xr <- xcmsRaw(fs, profstep = 0)

    ## Extracting the data from the xcmsRaw for do_findChromPeaks_centWave
    mzVals <- xr@env$mz
    intVals <- xr@env$intensity
    ## Define the values per spectrum:
    valsPerSpect <- diff(c(xr@scanindex, length(mzVals)))

    ## Calling the function. We're using a large value for noise to speed up
    ## the call in the example performance - in a real use case we would either
    ## set the value to a reasonable value or use the default value.
    res <- do_findChromPeaks_centWave(mz = mzVals, int = intVals,
    scantime = xr@scantime, valsPerSpect = valsPerSpect, noise = 10000)
    head(res)

[Package xcms version 3.2.0 Index]