peakPick-methods {Cardinal} | R Documentation |
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())