peakPick-methods: Peak pick an imaging dataset
In Cardinal: A mass spectrometry imaging toolbox for statistical analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

Apply peak picking to a mass spectrometry imaging dataset.

## S4 method for signature 'MSImagingExperiment'
peakPick(object, method = c("mad", "simple", "adaptive"), ...)

## S4 method for signature 'MSImageSet'
peakPick(object, method = c("simple", "adaptive", "limpic"),
    ...,
    pixel = pixels(object),
    plot = FALSE)

## Local maxima and SNR with noise based on local MAD
peakPick.mad(x, SNR=6, window=5, blocks=1, fun=mean, tform=diff, ...)

## Local maxima and SNR with constant noise based on SD
peakPick.simple(x, SNR=6, window=5, blocks=100, ...)

## Local maxima and SNR with adaptive noise based on SD
peakPick.adaptive(x, SNR=6, window=5, blocks=100, spar=1, ...)

## LIMPIC peak detection
peakPick.limpic(x, SNR=6, window=5, blocks=100, thresh=0.75, ...)

`object`	An imaging dataset.
`method`	The peak picking method to use.
`pixel`	The pixels to peak pick. If less than the extent of the dataset, this will result in a subset of the data being processed.
`plot`	Plot the mass spectrum for each pixel while it is being processed?
`...`	Additional arguments passed to the peak picking method.
`x`	The mass spectrum to be peak picked.
`SNR`	The minimum signal-to-noise ratio to be considered a peak.
`window`	The window width for seeking local maxima.
`blocks`	The number of blocks in which to divide the mass spectrum in order to calculate the noise.
`fun`	The function used to estimate centrality and average absolute deviation.
`tform`	A transformation to be applied to the mass spectrum before estimating noise.
`spar`	Smoothing parameter for the spline smoothing applied to the spectrum in order to decide the cutoffs for throwing away false noise spikes that might occur inside peaks.
`thresh`	The thresholding quantile to use when comparing slopes in order to throw away peaks that are too flat.

Peak picking is usually performed using the provided functions, but a user-created function can also be passed to method. In this case it should take the following arguments:

x: A numeric vector of intensities.
...: Additional arguments.

When applied to an MSImagingExperiment object, a user-created function should return a integer vector giving the indices of the detected peaks.

When applied to an MSImageSet object, a user-created function should return a list with two vectors of the same length as x:

peaks: A logical vector indicating peaks.
noise: A numeric vector with the estimated noise.

Internally, pixelApply is used to apply the peak picking. See its documentation page for more details on additional objects available to the environment installed to the peak picking function.

An object of the same class with the peak picked spectra. Note that the full mass range is retained and the peaks are unaligned, so peakAlign should be called before applying further methods.

Kylie A. Bemis

Mantini, D., Petrucci, F., Pieragostino, D., Del Boccio, P., Di Nicola, M., Di Ilio, C., et al. (2007). LIMPIC: a computational method for the separation of protein MALDI-TOF-MS signals from noise. BMC Bioinformatics, 8(101), 101. doi:10.1186/1471-2105-8-101

MSImagingExperiment, MSImageSet, peakAlign, peakFilter, peakBin, reduceDimension, pixelApply, process

setCardinalBPPARAM(SerialParam())

set.seed(2)
data <- simulateImage(preset=1, npeaks=10, dim=c(3,3))
data <- data[,pData(data)$circle]

# queue peak picking
data <- peakPick(data, method="simple", SNR=6)

# apply peak picking
data_peaks <- process(data, plot=interactive())

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