normalize-methods: Normalize an imaging dataset

In Cardinal: A mass spectrometry imaging toolbox for statistical analysis

Description

Apply normalization to the feature vectors of an imaging dataset.

Usage

## S4 method for signature 'SparseImagingExperiment'
normalize(object, method = c("tic", "rms", "reference"), ...)

## S4 method for signature 'MSImageSet'
normalize(object, method = "tic",
	...,
	pixel = pixels(object),
	plot = FALSE)

## Totial-ion-current normalization
normalize.tic(x, tic=length(x), ...)

## Root-mean-square normalization
normalize.rms(x, rms=1, ...)

## Reference normalization
normalize.reference(x, feature, scale=1, ...)

Arguments

object	An imaging dataset.
method	The normalization method to use.
pixel	The pixels to normalize. 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 normalization method.
x	The signal to be normalized.
tic	The value to which to normalize the total ion current.
rms	The value to which to normalize the root-mean-square.
feature	The feature to use as a reference for normalization.
scale	The value to which to normalize the reference.

Details

Normalization 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 normalization. See its documentation page for more details on additional objects available to the environment installed to the normalization function.

Value

An object of the same class with the normalized 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))
data <- data[,pData(data)$circle]

# queue normalization
data <- normalize(data, method="tic")

# apply normalization
data_normalized <- process(data)

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.