mzBin-methods: Mass bin an imaging dataset

In Cardinal: A mass spectrometry imaging toolbox for statistical analysis

Description

Apply mass binning to a mass spectrometry imaging dataset.

Usage

## S4 method for signature 'MSImagingExperiment,numeric'
mzBin(object, ref, tolerance = NA, units = c("ppm", "mz"), fun=sum, ...)

## S4 method for signature 'MSImagingExperiment,missing'
mzBin(object, from=min(mz(object)), to=max(mz(object)), by,
        resolution = NA, units = c("ppm", "mz"), fun=sum, ...)

Arguments

object	An imaging dataset.
ref	A reference to which the m/z values are binned.
tolerance	The half-width(s) of the bins. If this is NA, then automatically guess a resolution from the data.
from, to	The starting amd (maximal) end values of the sequence of m/z values.
by	The (approximate) interval between m/z values. For units="ppm", rather than an exact step size, this actually corresponds to a binwidth, where each element of the sequence is considered the center of a bin.
resolution	Another way to specify the interval between m/z values. For units="mz", this is the same as by. For units="ppm", this is the half-binwdith. If this is NA, then automatically guess a resolution from the data.
units	The units for by and resolution. Either parts-per-million or absolute m/z increments.
fun	The function used to summarize each mass bin.
...	Ignored.

Details

The reference masses are considered to be the center of each bin. The bin is then expanded on either side according to half the value of width, and the intensities in each bin are summarized by applying fun.

Internally, pixelApply is used to apply the binning. See its documentation page for more details.

Value

An object of the same class with the binned spectra.

Author(s)

Kylie A. Bemis

See Also

MSImagingExperiment, mzAlign, peakBin, reduceDimension, pixelApply, process

Examples

setCardinalBPPARAM(SerialParam())

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

# queue m/z binning
data <- mzBin(data, resolution=10, units="mz", fun=max)

# apply m/z binning
data_binned <- process(data, plot=interactive())

Cardinal documentation built on Nov. 8, 2020, 11:10 p.m.