smoothSignal-methods: Smooth the signals of a imaging dataset

In Cardinal: A mass spectrometry imaging toolbox for statistical analysis

Description

Apply smoothing to the feature vectors of an imaging dataset.

Usage

## S4 method for signature 'SparseImagingExperiment'
smooth(x, ...)

## S4 method for signature 'SparseImagingExperiment'
smoothSignal(object, method = c("gaussian", "sgolay", "ma"), ...)

## S4 method for signature 'MSImageSet'
smoothSignal(object, method = c("gaussian", "sgolay", "ma"),
    ...,
    pixel = pixels(object),
    plot = FALSE)

## Gaussian smoothing
smoothSignal.gaussian(x, sd=window/4, window=5, ...)

## Savitsky-Golay smoothing
smoothSignal.sgolay(x, order=3, window=order + 3 - order %% 2, ...)

## Moving average smoothing
smoothSignal.ma(x, coef=rep(1, window + 1 - window %% 2), window=5, ...)

Arguments

object	An imaging dataset.
method	The smoothing method to use.
pixel	The pixels to smooth. If less than the extent of the dataset, this will result in a subset of the data being processed.
plot	Plot each pixel while it is being processed?
...	Additional arguments passed to the smoothing method.
x	The signal or dataset to be smoothed.
sd	The standard deviation for the Gaussian kernel.
window	The smoothing window.
order	The order of the smoothing filter.
coef	The coefficients for the moving average filter.

Details

Smoothing 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.

A user-created function should return a numeric vector of the same length.

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

Value

An object of the same class with the smoothed spectra.

Author(s)

Kylie A. Bemis

See Also

MSImagingExperiment, MSImageSet, pixelApply, process

Examples

setCardinalBPPARAM(SerialParam())

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

# queue smoothing
data <- smoothSignal(data, method="ma", window=9)

# apply smoothing
data_smooth <- process(data, plot=interactive())

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