MALDIquant: detectPeaks-methods – R documentation

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detectPeaks-methods

Detects peaks in a MassSpectrum object.

Description

This method looks for peaks in mass spectrometry data (represented by a MassSpectrum object).
A peak is a local maximum above a user defined noise threshold.

Usage

## S4 method for signature 'MassSpectrum'
detectPeaks(object,
  halfWindowSize=20, method=c("MAD", "SuperSmoother"), SNR=2,
  ...)
## S4 method for signature 'list'
detectPeaks(object, ...)

Arguments

`object`	`MassSpectrum` object or a `list` of `MassSpectrum` objects.
`halfWindowSize`	`numeric`, half window size. The resulting window reaches from `mass[currentIndex-halfWindowSize]` to `mass[currentIndex+halfWindowSize]`. A local maximum have to be the highest one in the given window to be recognized as peak.
`method`	a noise estimation function; see `estimateNoise,MassSpectrum-method`.
`SNR`	single numeric value. `SNR` is an abbreviation for signal-to-noise-ratio. A local maximum has to be higher than `SNR*noise` to be recognize as peak.
`...`	arguments to be passed to `estimateNoise,MassSpectrum-method`. If `object` is a `list` `mc.cores` is also supported.

Value

Returns a MassPeaks object.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

Examples

## load package
library("MALDIquant")

## load example data
data("fiedler2009subset", package="MALDIquant")

## choose only the first mass spectrum
s <- fiedler2009subset[[1]]

## transform intensities
s <- transformIntensity(s, method="sqrt")

## smoothing spectrum
s <- smoothIntensity(s, method="MovingAverage")

## remove baseline
s <- removeBaseline(s)

## plot spectrum
plot(s)

## call peak detection
p <- detectPeaks(s)

## draw peaks on the plot
points(p)

## label 10 highest peaks
top10 <- intensity(p) %in% sort(intensity(p), decreasing=TRUE)[1:10]
labelPeaks(p, index=top10)

MALDIquant

Quantitative Analysis of Mass Spectrometry Data

v1.19.3

GPL (>= 3)

Authors

Sebastian Gibb [aut, cre] (<https://orcid.org/0000-0001-7406-4443>), Korbinian Strimmer [ths] (<https://orcid.org/0000-0001-7917-2056>)

Initial release

2019-05-12