peakBin-methods: Peak bin an imaging dataset

In Cardinal: A mass spectrometry imaging toolbox for statistical analysis

Description

Apply peak binning to a mass spectrometry imaging dataset.

Usage

## S4 method for signature 'MSImagingExperiment,numeric'
peakBin(object, ref, type=c("area", "height"),
        tolerance = NA, units = c("ppm", "mz"), ...)

## S4 method for signature 'MSImagingExperiment,missing'
peakBin(object, type=c("area", "height"),
        tolerance = NA, units = c("ppm", "mz"), ...)

Arguments

object	An imaging dataset.
ref	A reference to which the peaks are binned.
type	Should the summarized intensity of the peak by the maximum height of the peak or the area under the curve?
tolerance	The tolerance to be used when matching the m/z features in the dataset to the reference. If this is NA, then automatically guess a resolution from the data.
units	The units to use for the tolerance.
...	Ignored.

Details

Peak binning is performed by first matching the m/z-values in the dataset to those in the reference, and then finding the boundaries of the peak by detecting the nearest local minima. Then either the maximum height or the area under the curve of the peak are returned.

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 peaks.

Author(s)

Kylie A. Bemis

See Also

MSImagingExperiment, peakPick, peakAlign, peakFilter, reduceDimension, pixelApply, process

Examples

setCardinalBPPARAM(SerialParam())

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

# queue peak binning
data <- peakBin(data, ref=ref, type="height")

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

Example output

Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which.max, which.min

Loading required package: BiocParallel
Loading required package: EBImage
Loading required package: S4Vectors
Loading required package: stats4

Attaching package: ‘S4Vectors’

The following object is masked from ‘package:base’:

    expand.grid

Loading required package: ProtGenerics

Attaching package: ‘ProtGenerics’

The following object is masked from ‘package:stats’:

    smooth


Attaching package: ‘Cardinal’

The following object is masked from ‘package:stats’:

    filter

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