pickPeaks-methods {MSnbase} | R Documentation

## Peak Detection for 'MSnExp' or 'Spectrum' instances

### Description

This method performs a peak picking on individual spectra (`Spectrum` instances) or whole experiments (`MSnExp` instances) to create centroided spectra. For noisy spectra there are currently two different noise estimators available, the Median Absolute Deviation (`method = "MAD"`) and Friedman's Super Smoother (`method = "SuperSmoother"`), as implemented in the `MALDIquant::detectPeaks` and `MALDIquant::estimateNoise` functions respectively.

The method supports also to optionally refine the m/z value of the identified centroids by considering data points that belong (most likely) to the same mass peak. The m/z value is calculated as an intensity weighted average of the m/z values within the peak region. How the peak region is defined depends on the method chosen:

`refineMz = "kNeighbors"`: m/z values (and their respective intensities) of the`2 * k` closest signals to the centroid are used in the intensity weighted average calculation. The number of neighboring signals can be defined with the argument `k`.

`refineMz = "descendPeak"`: the peak region is defined by descending from the identified centroid/peak on both sides until the measured signal increases again. Within this defined region all measurements with an intensity of at least `signalPercentage` of the centroid's intensity are used to calculate the refined m/z. By default the descend is stopped when the first signal that is equal or larger than the last observed one is encountered. Setting `stopAtTwo = TRUE`, two consecutively increasing signals are required.

By default (`refineMz = "none"`, simply the m/z of the largest signal (the identified centroid) is reported. See below for examples.

### Methods

`signature(x = "MSnExp", halfWindowSize = "integer", method = "character", SNR = "numeric", verbose = "logical", refineMz = "character", ...)`

  Performs the peak picking for all spectra in an `MSnExp` instance. `method` could be `"MAD"` or `"SuperSmoother"`. `halfWindowSize` controls the window size of the peak picking algorithm. The resulting window size is `2 * halfWindowSize + 1`. The size should be nearly (or slightly larger) the FWHM (full width at half maximum). A local maximum is considered as peak if its intensity is `SNR` times larger than the estimated noise. `refineMz` allows to choose a method for an optional centroid m/z refinement (see description for more details). Choises are `"none"` (default, no m/z refinement), `"kNeighbors"` and `"descendPeak"`. The arguments `...` are passed to the noise estimator or m/z refinement functions. For the noise estimator functions, currenlty only the `method = "SuperSmoother"` accepts additional arguments, e.g. `span`. Please see `supsmu` for details. `refineMethod = "kNeighbors"` supports additional argument `k` and `refineMethod = "descendPeak"` arguments `signalPercentage` and `stopAtTwo`. See description above for more details.

  This method displays a progress bar if `verbose = TRUE`. Returns an `MSnExp` instance with centroided spectra.

`signature(x = "Spectrum", method = "character", halfWindowSize = "integer", ...)`

  Performs the peak picking for the spectrum (`Spectrum` instance). This method is the same as above but returns a centroided `Spectrum` instead of an `MSnExp` object. It has no `verbose` argument. Please read the details for the above `MSnExp` method.

### Author(s)

Sebastian Gibb <mail@sebastiangibb.de> with contributions from Johannes Rainer.

### References

S. Gibb and K. Strimmer. 2012. MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics 28: 2270-2271. http://strimmerlab.org/software/maldiquant/

### See Also

`clean`, `removePeaks` `smooth`, `estimateNoise` and `trimMz` for other spectra processing methods.

### Examples

    sp1 <- new("Spectrum1",
               intensity = c(1:6, 5:1),
               mz = 1:11,
               centroided = FALSE)
    sp2 <- pickPeaks(sp1)
    intensity(sp2)

    data(itraqdata)
    itraqdata2 <- pickPeaks(itraqdata)
    processingData(itraqdata2)

    ## Examples for refineMz:
    ints <- c(5, 3, 2, 3, 1, 2, 4, 6, 8, 11, 4, 7, 5, 2, 1, 0, 1, 0, 1, 1, 1, 0)
    mzs <- 1:length(ints)
    sp1 <- new("Spectrum1", intensity = ints, mz = mzs, centroided = FALSE)
    plot(mz(sp1), intensity(sp1), type = "h")

    ## Without m/z refinement:
    sp2 <- pickPeaks(sp1)
    points(mz(sp2), intensity(sp2), col = "darkgrey")
    ## Using k = 1, closest signals
    sp3 <- pickPeaks(sp1, refineMz = "kNeighbors", k = 1)
    points(mz(sp3), intensity(sp3), col = "green", type = "h")

    ## Using descendPeak requiring at least 50% or the centroid's intensity
    sp4 <- pickPeaks(sp1, refineMz = "descendPeak", signalPercentage = 50)
    points(mz(sp4), intensity(sp4), col = "red", type = "h")

[Package MSnbase version 2.18.0 Index]