Version:	1.22.3
Date:	2024-08-19
Title:	Quantitative Analysis of Mass Spectrometry Data
Depends:	R (≥ 4.0.0), methods
Imports:	parallel
Suggests:	knitr, testthat (≥ 0.8)
Description:	A complete analysis pipeline for matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) and other two-dimensional mass spectrometry data. In addition to commonly used plotting and processing methods it includes distinctive features, namely baseline subtraction methods such as morphological filters (TopHat) or the statistics-sensitive non-linear iterative peak-clipping algorithm (SNIP), peak alignment using warping functions, handling of replicated measurements as well as allowing spectra with different resolutions.
License:	GPL (≥ 3)
URL:	https://strimmerlab.github.io/software/maldiquant/, https://github.com/sgibb/MALDIquant/
BugReports:	https://github.com/sgibb/MALDIquant/issues/
LazyLoad:	yes
VignetteBuilder:	knitr
RoxygenNote:	7.3.2
Encoding:	UTF-8
NeedsCompilation:	yes
Packaged:	2024-08-19 20:58:22 UTC; sebastian
Author:	Sebastian Gibb [aut, cre], Korbinian Strimmer [ths], Sigurdur Smarason [ctb], Laurent Gatto [ctb], Paolo Inglese [ctb]
Maintainer:	Sebastian Gibb <mail@sebastiangibb.de>
Repository:	CRAN
Date/Publication:	2024-08-19 23:00:03 UTC

Quantitative Analysis of Mass Spectrometry Data

Description

MALDIquant provides a complete analysis pipeline for matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) and other two-dimensional mass spectrometry data.

In addition to commonly used plotting and processing methods it includes distinctive features, namely baseline subtraction methods such as morphological filters (TopHat) or the statistics-sensitive non-linear iterative peak-clipping algorithm (SNIP), peak alignment using warping functions, handling of replicated measurements as well as allowing spectra with different resolutions.

For a first overview see vignette("MALDIquant-intro", package="MALDIquant") and/or run demo("MALDIquant").

Details

Main classes:

• MassPeaks: Represents a peak list of a single spectrum.

• MassSpectrum: Represents a single spectrum.

The accompanying website (see below) provides example R scripts to illustrate the functionality of this package, too.

Author(s)

Sebastian Gibb

Maintainer: Sebastian Gibb mail@sebastiangibb.de

References

S. Gibb and K. Strimmer. 2012.
MALDIquant: a versatile R package for the analysis of mass spectrometry data.
Bioinformatics 28: 2270-2271.
doi:10.1093/bioinformatics/bts447

Website: https://strimmerlab.github.io/software/maldiquant/

See Also

• Introduction: vignette("MALDIquant-intro", package="MALDIquant").

• Run demo files: demo("MALDIquant").

• List all available manual pages: library(help="MALDIquant").

• MALDIquant website: https://strimmerlab.github.io/software/maldiquant/.

• more MALDIquant examples and complete analyses: https://github.com/sgibb/MALDIquantExamples/.

Class "AbstractMassObject"

Description

AbstractMassObject is an abstract (means pure virtual) class. It is the parent class of MassSpectrum and MassPeaks. It shouldn't create or handle by the user because it is for internal use only.

Derived classes

MassPeaks, MassSpectrum

Slots

mass:

numeric, mass or mass-to-charge ratio

intensity:

numeric, intensities for measured mass-to-charge ratios

metaData:

list, some metadata to describe the spectrum

Methods

[

signature(x = "AbstractMassObject", i = "numeric"): Extracts a range of an AbstractMassObject object and returns a new one.

as.matrix

signature(x = "AbstractMassObject"): Converts an AbstractMassObject object to a matrix with 2 columns (mass, intensity).

coordinates

signature(object = "AbstractMassObject"): Accessor function for coordinates stored in object generated from imaging mass spectrometry data.

coordinates<-

signature(object = "AbstractMassObject", value = "numeric|matrix") Replacement function for coordinates used in imaging mass spectrometry datasets.

intensity

signature(object = "AbstractMassObject"): Accessor function for slot intensity.

intensity<-

signature(object = "AbstractMassObject", value = "numeric") Replacement function for slot intensity.

isEmpty

signature(object = "AbstractMassObject"): Returns TRUE if length of intensity is 0 or all intensity values are 0.

length

signature(x = "AbstractMassObject"): Returns length of slot intensity.

lines

signature(x = "AbstractMassObject"): Extented function for adding AbstractMassObject object as a line to a specific plot. See lines for details.

mass

signature(object = "AbstractMassObject"): Accessor function for slot mass.

mass<-

signature(object = "AbstractMassObject", value = "numeric") Replacement function for slot mass.

mz

signature(object = "AbstractMassObject"): Accessor function for slot mass.

mz<-

signature(object = "AbstractMassObject", value = "numeric") Replacement function for slot mass.

metaData

signature(object = "AbstractMassObject"): Accessor function for slot metaData.

metaData<-

signature(object = "AbstractMassObject"): Replacement function for slot metaData.

plot

signature(x = "AbstractMassObject", y = "missing"): Extented function for plotting an AbstractMassObject object. See plot,AbstractMassObject,missing-method for details.

points

signature(x = "AbstractMassObject"): Extented function for adding AbstractMassObject object as points to a specific plot. See points for details.

trim

signature(object = "AbstractMassObject", range = "numeric"): Trim an AbstractMassObject object. See trim,AbstractMassObject,numeric-method for details.

transformIntensity

signature(object = "AbstractMassObject"): Transforms the intensities of an AbstractMassObject object. See transformIntensity,AbstractMassObject-method for details.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

MassPeaks, MassSpectrum, plot,AbstractMassObject,missing-method, transformIntensity,AbstractMassObject-method, trim,AbstractMassObject,numeric-method

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create example spectrum
s <- createMassSpectrum(mass=1:10, intensity=11:20,
                        metaData=list(name="Example Spectrum"))

## get intensity
intensity(s)

## get mass
mass(s)

## get metaData
metaData(s)

## replace metaData
metaData(s) <- list(name="Spectrum")

## trim spectrum
trim(s, c(2, 9))

## select a range
s[3:6]

Removed Functions/Methods in Package MALDIquant

Description

These functions/methods are removed.

Details

plotImsSlice

use plotMsiSlice instead.

totalIonCurrent

signature(object = "MassPeaks"): removed.

totalIonCurrent<-

signature(object = "AbstractMassObject", value = "numeric"): use calibrateIntensity instead.

calibrate

signature(x = "matrix"): use calibrateIntensity instead.

standardizeTotalIonCurrent

signature(object = "list"): use calibrateIntensity instead.

ltrim

signature(object = "AbstractMassObject", minMass = "numeric"): use trim instead.

rtrim

use trim instead.

mergeMassSpectra

use averageMassSpectra instead.

movingAverage

use smoothIntensity instead.

savitzkyGolay

use smoothIntensity instead.

isMassObject

removed.

isMassObjectList

removed.

intensityMatrix

does not support MassSpectrum objects anymore.

Deprecated Functions/Methods in Package MALDIquant

Description

These functions/methods are provided for compatibility with older versions MALDIquant only, and may be defunct as soon as the next release.

See Also

Deprecated

Parallel Support in Package MALDIquant

Description

MALDIquant offers multi-core support using mclapply and mcmapply. This approach is limited to unix-based platforms.

Please note that not all functions benfit from parallelisation. Often the overhead to create/copy objects outrun the time saving of parallel runs. This is true for functions that are very fast to compute (e.g. sqrt-transformation). That's why the default value for the mc.cores argument in all functions is 1L. It depends on the size of the dataset which step (often only removeBaseline and detectPeaks) benefits from parallelisation.
In general it is faster to encapsulate the complete workflow into a function and parallelise it using mclapply instead of using the mc.cores argument of each method. The reason is the reduced overhead for object management (only one split/combine is needed instead of doing these operations in each function again and again).

Details

The following functions/methods support the mc.cores argument:

• trim,list,numeric-method

• transformIntensity,list-method

• smoothIntensity,list-method

• removeBaseline,list-method

• calibrateIntensity,list-method

• detectPeaks,list-method

• alignSpectra

• averageMassSpectra

• mergeMassPeaks

See Also

mclapply, mcmapply

Examples

## Not run: 
## load package
library("MALDIquant")

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

## run single-core baseline correction
print(system.time(
  b1 <- removeBaseline(fiedler2009subset, method="SNIP")
))

if(.Platform$OS.type == "unix") {
  ## run multi-core baseline correction
  print(system.time(
    b2 <- removeBaseline(fiedler2009subset, method="SNIP", mc.cores=2)
  ))
  stopifnot(all.equal(b1, b2))
}

## parallelise complete workflow
workflow <- function(spectra, cores) {
  s <- transformIntensity(spectra, method="sqrt", mc.cores=cores)
  s <- smoothIntensity(s, method="SavitzkyGolay", halfWindowSize=10,
                       mc.cores=cores)
  s <- removeBaseline(s, method="SNIP", iterations=100, mc.cores=cores)
  s <- calibrateIntensity(s, method="TIC", mc.cores=cores)
  detectPeaks(s, method="MAD", halfWindowSize=20, SNR=2, mc.cores=cores)
}

if(.Platform$OS.type == "unix") {
  ## parallelise the complete workflow is often faster because the overhead is
  ## reduced
  print(system.time(
    p1 <- unlist(parallel::mclapply(fiedler2009subset,
                                    function(x)workflow(list(x), cores=1),
                                    mc.cores=2), use.names=FALSE)
  ))
  print(system.time(
    p2 <- workflow(fiedler2009subset, cores=2)
  ))
  stopifnot(all.equal(p1, p2))
}

## End(Not run)

Class "MassPeaks"

Description

MassPeaks represents extracted peaks of a single spectrum of a MALDI-TOF mass spectrometry measurement.

Objects from the Class

createMassPeaks: Creates a MassPeaks object.

Extends

Class AbstractMassObject, directly.

Slots

snr:

vector, signal-to-noise ratio

Methods

labelPeaks

signature(x = "MassPeaks"): Draws peak labels to plot. See labelPeaks,MassPeaks-method for details.

monoisotopicPeaks

signature(x = "MassPeaks"): Finds monoisotopic peaks in peak lists. See monoisotopicPeaks,MassPeaks-method for details.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

createMassPeaks, detectPeaks,MassSpectrum-method, labelPeaks,MassPeaks-method, AbstractMassObject

Website: https://strimmerlab.github.io/software/maldiquant/

Class "MassSpectrum"

Description

MassSpectrum represents a single spectrum of a MALDI-TOF mass spectrometry measurement. It provides an easy framework for doing some preprocessing steps like peak detection, baseline correction and much more.

Objects from the Class

createMassSpectrum: Creates a MassSpectrum object.

Extends

Class AbstractMassObject, directly.

Methods

calibrateIntensity

signature(x = "MassSpectrum"): Calibrates the intensity of a MassSpectrum object. See calibrateIntensity,MassSpectrum-method for details.

detectPeaks

signature(x = "MassSpectrum"): Look for local maxima and estimate noise to extract peaks out of a MassSpectrum object. See detectPeaks,MassSpectrum-method for details.

estimateBaseline

signature(x = "MassSpectrum"): Estimates the baseline of a MassSpectrum object. See estimateBaseline,MassSpectrum-method for details.

estimateNoise

signature(x = "MassSpectrum"): Estimates the noise of a MassSpectrum object. See estimateNoise,MassSpectrum-method for details.

isRegular

signature(object = "MassSpectrum"): Returns FALSE if the frequency of mass values with irregular intervals is greater than threshold (because object was measured in centroid mode or some intensity values were filtered).

removeBaseline

signature(x = "MassSpectrum"): Estimates and removes the baseline of a MassSpectrum object. See removeBaseline,MassSpectrum-method for details.

smoothIntensity

signature(object = "MassSpectrum"): Smoothes the intensities of an MassSpectrum object. See smoothIntensity,MassSpectrum-method for details.

totalIonCurrent

signature(object = "MassSpectrum"): Accessor function for Total Ion Current (TIC, area under the curve).

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

createMassSpectrum, calibrateIntensity,MassSpectrum-method, detectPeaks,MassSpectrum-method, estimateBaseline,MassSpectrum-method, estimateNoise,MassSpectrum-method, removeBaseline,MassSpectrum-method, smoothIntensity,MassSpectrum-method, AbstractMassObject

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create a MassSpectrum object by default constructor
s <- createMassSpectrum(mass=1:100, intensity=rnorm(100)^2,
                        metaData=list(name="example"))

## show some details
s

## plot spectrum
plot(s)

## get TIC
totalIonCurrent(s)

## modify intensity and metaData
intensity(s)[1:50] <- 0
metaData(s) <- list(name="modified example")

## plot again
plot(s)

Align MassSpectrum objects.

Description

This function aligns a list of MassSpectrum objects (spectra alignment is also known as warping/phase correction).

Usage

alignSpectra(spectra, halfWindowSize=20, noiseMethod="MAD", SNR=2,
  reference, tolerance=0.002, warpingMethod="lowess",
  allowNoMatches=FALSE, emptyNoMatches=FALSE, ...)

Arguments

Details

alignSpectra is a wrapper function around detectPeaks, determineWarpingFunctions and warpMassSpectra. Please call these functions manually if you need finer control (e.g. plotting of warping functions).

Value

Returns a list of aligned MassSpectrum objects.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

detectPeaks, determineWarpingFunctions, referencePeaks, warpMassSpectra, MassSpectrum

demo("warping")

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

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

## running typical workflow

## transform intensities
spectra <- transformIntensity(fiedler2009subset, method="sqrt")

## smooth spectra
spectra <- smoothIntensity(spectra, method="MovingAverage")

## baseline correction
spectra <- removeBaseline(spectra)

## align spectra
spectra <- alignSpectra(spectra)

Averages MassSpectrum objects.

Description

This function averages MassSpectrum objects.

Usage

averageMassSpectra(l, labels, method=c("mean", "median", "sum"), ...)

Arguments

Details

The mass of the averaged MassSpectrum object will be the mass of the first non-empty MassSpectrum object (of each group).

Value

Returns a single (no labels given) or a list (labels given) of averaged MassSpectrum objects.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

MassSpectrum, mergeMassPeaks

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create four MassSpectrum objects and add them to a list
s <- list(createMassSpectrum(mass=1:5, intensity=1:5),
          createMassSpectrum(mass=1:5, intensity=1:5),
          createMassSpectrum(mass=1:5, intensity=6:10),
          createMassSpectrum(mass=1:5, intensity=6:10))

## average all four MassSpectrum objects into a single new one
## by sum their intensities
## (no labels, returns only one new MassSpectrum object)
summedSpectra <- averageMassSpectra(s, method="sum")

## only average MassSpectrum objects in a group
## (e.g. useful for technical replicates)
## (two different labels, returns a list of two new MassPeaks objects)
groups <- factor(c("a", "a", "b", "b"), levels=c("a", "b"))
averagedSpectra <- averageMassSpectra(s, labels=groups, method="mean")

Align Peaks into discrete bins.

Description

This function looks for similar peaks (mass) across MassPeaks objects and equalizes their mass.

Usage

binPeaks(l, method=c("strict", "relaxed", "reference"), tolerance=0.002)

Arguments

Details

The algorithm is based on the following workflow:

1. Put all mass in a sorted vector.

2. Calculate differences between each neighbor.

3. Divide the mass vector at the largest gap (largest difference) and form a left and a right bin.

4. Rerun step 3 for the left and/or the right bin if they don't fulfill the following criteria:

   • All peaks in a bin are near to the mean (method == "strict" or method == "relaxed") (abs(mass-meanMass)/meanMass < tolerance) or the reference mass (method == "reference"; abs(mass-reference)/reference < tolerance).

   • method == "strict": The bin doesn't contain two or more peaks of the same sample.

method == "strict": The new peak positions (mass value) are the mean mass of a bin.
method == "relaxed": The new peak positions for the highest peaks of each sample in a bin are generated by the mean mass of this peaks. The lower peaks are not changed. method == "reference": The new peak positions for the highest peaks of each sample in a bin are generated by the mass of peaks of the first MassPeaks object. Lower peaks are not changed.

Value

Returns a list of mass adjusted MassPeaks objects.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

intensityMatrix, MassPeaks

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create two MassPeaks objects
p <- list(createMassPeaks(mass=seq(100, 500, 100), intensity=1:5),
          createMassPeaks(mass=c(seq(100.2, 300.2, 100), 395), intensity=1:4))

binnedPeaks <- binPeaks(p, tolerance=0.002)

## compare result
iM1 <- intensityMatrix(p)
iM2 <- intensityMatrix(binnedPeaks)

all(dim(iM1) == c(2, 9)) # TRUE
all(dim(iM2) == c(2, 6)) # TRUE

show(iM2)

## increase tolerance
binnedPeaks <- binPeaks(p, tolerance=0.1)

iM3 <- intensityMatrix(binnedPeaks)

all(dim(iM3) == c(2, 5)) # TRUE

show(iM3)

## differences between "strict" and "relaxed"
p <- c(createMassPeaks(mass=c(1, 1.01, 3), intensity=c(2, 1, 1)),
       createMassPeaks(mass=c(0.99, 3), intensity=rep(1, 2)),
       createMassPeaks(mass=c(1.02, 3), intensity=rep(1, 2)))

intensityMatrix(binPeaks(p, method="strict", tolerance=0.05))
intensityMatrix(binPeaks(p, method="relaxed", tolerance=0.05))

## use a reference
ref <- createMassPeaks(mass=c(1, 3), intensity=rep(1, 2))
## include the reference
intensityMatrix(binPeaks(c(ref, p), method="reference", tolerance=0.05))
## drop the reference
intensityMatrix(binPeaks(c(ref, p), method="reference", tolerance=0.05)[-1])

Calibrates intensities of a MassSpectrum object.

Description

This function calibrates (normalize) intensities of MassSpectrum objects.

Usage

## S4 method for signature 'MassSpectrum'
calibrateIntensity(object,
  method=c("TIC", "PQN", "median"), range, ...)
## S4 method for signature 'list'
calibrateIntensity(object,
  method=c("TIC", "PQN", "median"), range, ...)

Arguments

Details

A number of different calibration methods are provided:

"TIC":

The TIC (Total Ion Current) of a MassSpectrum object is set to one. If range is given the TIC is only calculated for the intensities in the specified mass range.

"PQN":

The PQN (Probabilistic Quotient Normalization) is described in Dieterle et al 2006. calibrateIntensity uses the following algorithm:

1. Calibrate all spectra using the "TIC" calibration.

2. Calculate a median reference spectrum.

3. Calculate the quotients of all intensities of the spectra with those of the reference spectrum.

4. Calculate the median of these quotients for each spectrum.

5. Divide all intensities of each spectrum by its median of quotients.

"median":

The median of intensities of a MassSpectrum object is set to one.

Value

Returns a modified MassSpectrum object with calibrated intensities.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

References

F. Dieterle, A. Ross, G. Schlotterbeck, and Hans Senn. 2006. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Analytical Chemistry 78(13): 4281-4290.

See Also

MassSpectrum

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

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

## baseline correction
b <- removeBaseline(fiedler2009subset)

## calibrate intensity values
calibrateIntensity(b, method="TIC")

## calibrate intensity values using TIC for a specific mass range
calibrateIntensity(b, method="TIC", range=c(3000, 5000))

Creates a MassPeaks object.

Description

This function creates a MassPeaks object. Normally it shouldn't called by the user. Try detectPeaks,MassSpectrum-method instead.

Usage

createMassPeaks(mass, intensity, snr=rep.int(NA_real_, length(intensity)),
                metaData=list())

Arguments

Value

Returns a MassPeaks object.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

detectPeaks,MassSpectrum-method, MassPeaks

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create a MassPeaks object by default constructor
s <- createMassPeaks(mass=1:100, intensity=rnorm(100)^2,
                     metaData=list(name="example peaks"))

## show some details
s

Creates a MassSpectrum object.

Description

This function creates a MassSpectrum object.

Usage

createMassSpectrum(mass, intensity, metaData=list())

Arguments

Value

Returns a MassSpectrum object.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

MassSpectrum

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create a MassSpectrum object by default constructor
s <- createMassSpectrum(mass=1:100, intensity=rnorm(100)^2,
                        metaData=list(name="example spectrum"))

## show some details
s

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

Value

Returns a MassPeaks object.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

MassPeaks, MassSpectrum, estimateNoise,MassSpectrum-method

demo("peaks")

Website: https://strimmerlab.github.io/software/maldiquant/

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)

Determine warping functions of MassPeaks objects.

Description

This function determines a warping function for a list of AbstractMassObject objects (warping is also known as phase correction/spectra alignment).

Usage

determineWarpingFunctions(l, reference, tolerance=0.002,
                          method=c("lowess", "linear", "quadratic", "cubic"),
                          allowNoMatches=FALSE,
                          plot=FALSE, plotInteractive=FALSE, ...)

Arguments

Details

warpingFunction: determineWarpingFunctions estimates a warping function to overcome the difference between mass in reference and in the current sample. To calculate the differences each reference peak would match with the highest sample peak in the nearer neighborhood (defined by mass of reference peak*tolerance).
allowNoMatches: If allowNoMatches is TRUE a warning instead of an error is thrown if an MassPeaks object could not match to the reference. The returned list of warping functions will contain NA for this object (same index in the list). plotInteractive: If plot is TRUE a lot of output is created (each sample in l gets its own plot). That's why an non-interactive devices is recommended:

## create a device
pdf()
## calculate warping functions
w <- determineWarpingFunctions(p, plot=TRUE)
## close device
dev.off()
  

Value

Returns a list of individual warping functions. The attribute nmatch contains the number of matches of each MassPeaks element in l against reference.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

referencePeaks, warpMassPeaks, warpMassSpectra, MassPeaks

demo("warping")

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create a reference MassPeaks object
r <- createMassPeaks(mass=1:5, intensity=1:5)

## create test samples
p <- list(createMassPeaks(mass=((1:5)*1.01), intensity=1:5),
          createMassPeaks(mass=((1:5)*0.99), intensity=1:5))

## create an interactive device with 2 rows
par(mfrow=c(2, 1))
## calculate warping function
## (using a linear function as basic warping function)
## and show warping plot
w <- determineWarpingFunctions(p, tolerance=0.02, method="linear",
                               plot=TRUE, plotInteractive=TRUE)
par(mfrow=c(1, 1))

## access number of matches
attr(w, "nmatch")

## w contains the individual warping functions
warpedPeaks <- warpMassPeaks(p, w)

## compare results
all(mass(r) == mass(warpedPeaks[[1]])) # TRUE
all(mass(r) == mass(warpedPeaks[[2]])) # TRUE



## realistic example

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

## running typical workflow

## use only four spectra of the subset
spectra <- fiedler2009subset[1:4]

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

## smooth spectra
spectra <- smoothIntensity(spectra, method="MovingAverage")

## baseline correction
spectra <- removeBaseline(spectra)

## detect peaks
peaks <- detectPeaks(spectra)

## create an interactive device with 2 rows
par(mfrow=c(4, 1))
## calculate warping functions (using LOWESS based basic function [default])
w <- determineWarpingFunctions(peaks, plot=TRUE, plotInteractive=TRUE)
par(mfrow=c(1, 1))



## realistic example with user defined reference/calibration peaks

## use the workflow above for fiedler2009subset

## create reference peaks
refPeaks <- createMassPeaks(mass=c(1207, 1264, 1351, 1466, 1616, 2769, 2932,
                                   3191, 3262, 4091, 4209, 5904, 7762, 9285),
                            intensity=rep(1, 14))

## create an interactive device with 2 rows
par(mfrow=c(4, 1))
## calculate warping functions (using a quadratic function as basic function)
w <- determineWarpingFunctions(peaks, reference=refPeaks, method="quadratic",
                               plot=TRUE, plotInteractive=TRUE)
par(mfrow=c(1, 1))

Estimates the baseline of a MassSpectrum object.

Description

This method estimates the baseline of mass spectrometry data (represented by a MassSpectrum object).

Usage

## S4 method for signature 'MassSpectrum'
estimateBaseline(object,
  method=c("SNIP", "TopHat", "ConvexHull", "median"),
  ...)

Arguments

Details

"SNIP":

This baseline estimation is based on the Statistics-sensitive Non-linear Iterative Peak-clipping algorithm (SNIP) described in Ryan et al 1988.

The algorithm based on the following equation:

y_i(k) = \min \{ y_i, \frac{(y_{i-k}+y_{i+k})}{2} \}

It has two additional arguments namely iterations and decreasing. iterations controls the window size (k; similar to halfWindowSize in "TopHat", "Median") of the algorithm. The resulting window reaches from mass[cur_index-iterations] to mass[cur_index+iterations].

decreasing: In Morhac 2009 a decreasing clipping window is suggested to get a smoother baseline. For decreasing = TRUE (decreasing = FALSE) k=iterations is decreased (increased) by one until zero (iterations) is reached. The default setting is decreasing = TRUE.

"TopHat":

This algorithm applies a moving minimum (erosion filter) and subsequently a moving maximum (dilation filter) filter on the intensity values. The implementation is based on van Herk 1996. It has an additional halfWindowSize argument determining the half size of the moving window for the TopHat filter. The resulting window reaches from mass[cur_index-halfWindowSize] to mass[cur_index+halfWindowSize].

"ConvexHull":

The baseline estimation is based on a convex hull constructed below the spectrum.

"median":

This baseline estimation uses a moving median. It is based on runmed. The additional argument halfWindowSize corresponds to the k argument in runmed (k = 2 * halfWindowSize + 1) and controls the half size of the moving window. The resulting window reaches from mass[cur_index-halfWindowSize] to mass[cur_index+halfWindowSize].

Value

Returns a two column matrix (first column: mass, second column: intensity) of the estimated baseline.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

References

"SNIP":
C.G. Ryan, E. Clayton, W.L. Griffin, S.H. Sie, and D.R. Cousens. 1988. Snip, a statistics-sensitive background treatment for the quantitative analysis of pixe spectra in geoscience applications. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 34(3): 396-402.

M. Morhac. 2009. An algorithm for determination of peak regions and baseline elimination in spectroscopic data. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 600(2), 478-487.

"TopHat":
M. van Herk. 1992. A Fast Algorithm for Local Minimum and Maximum Filters on Rectangular and Octagonal Kernels. Pattern Recognition Letters 13.7: 517-521.

J. Y. Gil and M. Werman. 1996. Computing 2-Dimensional Min, Median and Max Filters. IEEE Transactions: 504-507.

"ConvexHull":
Andrew, A. M. 1979. Another efficient algorithm for convex hulls in two dimensions. Information Processing Letters, 9(5), 216-219.

See Also

MassSpectrum, removeBaseline,MassSpectrum-method

demo("baseline")

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

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

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


## SNIP
plot(s)

## estimate baseline
b <- estimateBaseline(s, method="SNIP", iterations=100)

## draw baseline on the plot
lines(b, col="red")


## TopHat
plot(s)

## estimate baseline (try different parameters)
b1 <- estimateBaseline(s, method="TopHat", halfWindowSize=75)
b2 <- estimateBaseline(s, method="TopHat", halfWindowSize=150)

## draw baselines on the plot
lines(b1, col=2)
lines(b2, col=3)

## draw legend
legend(x="topright", lwd=1, legend=paste0("halfWindowSize=", c(75, 150)),
       col=c(2, 3))


## ConvexHull
plot(s)

## estimate baseline
b <- estimateBaseline(s, method="ConvexHull")

## draw baseline on the plot
lines(b, col="red")


## Median
plot(s)

## estimate baseline
b <- estimateBaseline(s, method="median")

## draw baseline on the plot
lines(b, col="red")

Estimates the noise of a MassSpectrum object.

Description

This method estimates the noise of mass spectrometry data (represented by a MassSpectrum object).

Usage

## S4 method for signature 'MassSpectrum'
estimateNoise(object,
  method=c("MAD", "SuperSmoother"),
  ...)

Arguments

Details

"MAD":

This function estimates the noise of mass spectrometry data by calculating the median absolute deviation, see also mad.

"SuperSmoother":

This function estimates the noise of mass spectrometry data using Friedman's Super Smoother. Please refer supsmu for details and additional arguments.

Value

Returns a two column matrix (first column: mass, second column: intensity) of the estimated noise.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

MassSpectrum, detectPeaks,MassSpectrum-method, mad, supsmu

Website: https://strimmerlab.github.io/software/maldiquant/

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")

## remove baseline
s <- removeBaseline(s)

## plot spectrum
plot(s)

## estimate noise
nm <- estimateNoise(s, method="MAD")
nss <- estimateNoise(s, method="SuperSmoother")

## draw noise on the plot
lines(nm, col=2)
lines(nss, col=4)

## draw legend
legend(x="topright", lwd=1, legend=c("MAD", "SuperSmoother"),
       col=c(2, 4))

Example Mass Spectra (raw)

Description

This dataset contains 16 example mass spectra. It is used to demonstrate the usage of MALDIquant-package.

Usage

data(fiedler2009subset)

Format

A list containing 16 MassSpectrum-class objects.

Details

The dataset is a subset of data used in Fiedler et al 2009.
It contains spectra of 8 different patients (each one has 2 technical replicates).

References

G.M. Fiedler, A.B. Leichtle, J. Kase, S. Baumann, U. Ceglarek, K. Felix, T. Conrad, H. Witzigmann, A. Weimann, C. Schütte, J. Hauss, M. Büchler and J. Thiery
“Serum Peptidome Profiling Revealed Platelet Factor 4 as a Potential Discriminating Peptide Associated with Pancreatic Cancer”
Clinical Cancer Research, 11(15): 3812-3819, 2009
ISSN 1557-3265; doi:10.1158/1078-0432.CCR-08-2701

See Also

MassSpectrum-class

Website: https://strimmerlab.github.io/software/maldiquant/

Removes less frequent peaks.

Description

This function removes infrequently occuring peaks in a list of MassPeaks objects.

Usage

filterPeaks(l, minFrequency, minNumber, labels, mergeWhitelists=FALSE)

Arguments

Details

For mergeWhitelists=FALSE the filtering uses a separate peak whitelist for each group specified by labels, and is done independently in each group. For mergeWhitelists=TRUE the peak whitelists are combined, which means that peaks that occur frequently in at least one group are also kept in all other groups.

If both minFrequency and minNumber arguments are specified the more stringent threshold is used.

Value

Returns a list of filtered MassPeaks objects.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

intensityMatrix, MassPeaks

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create four MassPeaks objects and add them to the list
p <- list(createMassPeaks(mass=1:2, intensity=1:2),
          createMassPeaks(mass=1:3, intensity=1:3),
          createMassPeaks(mass=1:4, intensity=1:4),
          createMassPeaks(mass=1:5, intensity=1:5))

## only keep peaks which occur in all MassPeaks objects
filteredPeaks <- filterPeaks(p, minFrequency=1)

## compare result
intensities <- intensityMatrix(filteredPeaks)

## peaks at mass 3,4,5 are removed
all(dim(intensities) == c(4, 2)) # TRUE
all(intensities[,1] == 1)        # TRUE
all(intensities[,2] == 2)        # TRUE

## only keep peaks which occur in all MassPeaks objects in a group
## (e.g. useful for technical replicates)
groups <- factor(c("a", "a", "b", "b"), levels=c("a", "b"))
filteredPeaks <- filterPeaks(p, minFrequency=1, labels=groups)

## peaks at mass 3 were removed in group "a"
filteredPeaks[groups == "a"]

## peaks at mass 5 were removed in group "b"
filteredPeaks[groups == "b"]

## only keep peaks which occur at least twice in a group
groups <- factor(c("a", "a", "b", "b", "b"), levels=c("a", "b"))
filteredPeaks <- filterPeaks(c(p, p[[3]]), minNumber=2, labels=groups)

## peaks at mass 3 were removed in group "a"
filteredPeaks[groups == "a"]

## peaks at mass 5 were removed in group "b"
filteredPeaks[groups == "b"]

## apply different minFrequency arguments to each group
groups <- factor(c("a", "a", "b", "b", "b"), levels=c("a", "b"))
filteredPeaks <- filterPeaks(c(p, p[[3]]), minFrequency=c(1, 2/3), labels=groups)
intensityMatrix(filteredPeaks)
#     1 2  3  4
#[1,] 1 2 NA NA
#[2,] 1 2 NA NA
#[3,] 1 2  3  4
#[4,] 1 2  3  4
#[4,] 1 2  3  4


## demonstrate the use of mergeWhitelists
groups <- factor(c("a", "a", "b", "b"), levels=c("a", "b"))

## default behaviour
filteredPeaks <- filterPeaks(p, minNumber=2, labels=groups)
intensityMatrix(filteredPeaks)
#     1 2  3  4
#[1,] 1 2 NA NA
#[2,] 1 2 NA NA
#[3,] 1 2  3  4
#[4,] 1 2  3  4

## use mergeWhitelists=TRUE to keep peaks of group "a" that match all filtering
## criteria in group "b"
## (please note that mass == 3 is not removed in the second MassPeaks object)
filteredPeaks <- filterPeaks(p, minNumber=2, labels=groups,
                             mergeWhitelists=TRUE)
intensityMatrix(filteredPeaks)
#     1 2  3  4
#[1,] 1 2 NA NA
#[2,] 1 2  3 NA
#[3,] 1 2  3  4
#[4,] 1 2  3  4

Finds or removes empty AbstractMassObject objects in a list.

Description

These functions looks for empty AbstractMassObject objects in a list.

Usage

findEmptyMassObjects(l)

removeEmptyMassObjects(l)

Arguments

Value

findEmptyMassObjects: Returns a vector of indices referring to empty AbstractMassObject objects.

removeEmptyMassObjects: Returns a list of AbstractMassObject objects but without empty ones.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

isEmpty,AbstractMassObject-method, AbstractMassObject

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create list
peakList <- list()

## create two MassPeaks objects and add them to the list
peakList[[1]] <- createMassPeaks(mass=1:100, intensity=1:100,
                                 metaData=list(name="example 1"))
peakList[[2]] <- createMassPeaks(mass=1:100, intensity=1:100,
                                 metaData=list(name="example 2"))

## find empty objects (there should not be any one)
findEmptyMassObjects(peakList)

## add an empty MassPeaks object to the list
peakList[[3]] <- createMassPeaks(mass=double(), intensity=double(),
                                 metaData=list(name="empty MassPeaks object"))

## look for empty objects (isEmptyIdx == 3)
(isEmptyIdx <- findEmptyMassObjects(peakList))

## to remove all empty MassObjects from a list
length(peakList) # 3
peakList <- removeEmptyMassObjects(peakList)
length(peakList) # 2; WARNING: all indices could changed

Converts a list of MassPeaks objects into a matrix.

Description

This function converts a list of MassPeaks objects into a matrix.

Usage

intensityMatrix(peaks, spectra)

Arguments

Details

peaks have to be binned by binPeaks before calling intensityMatrix.

Value

Returns a matrix containing intensities of all MassPeaks objects of peaks and interpolated intensity values for missing peaks if spectra was given or NA otherwise.
The matrix has length(peaks) rows (one row for each sample) and length(unique(mass)) columns. There is an additional attribute "mass" that stores the mass values.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

binPeaks, MassPeaks, MassSpectrum

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create example MassPeaks objects
p <- list(createMassPeaks(mass=1:4,
                          intensity=11:14,
                          metaData=list(name="test mass peaks 1")),
          createMassPeaks(mass=2:5,
                          intensity=22:25,
                          metaData=list(name="test mass peaks 2")))

## converts MassPeaks objects into a matrix
intensityMatrix(p)


## realistic example

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

## transform intensities
s <- transformIntensity(fiedler2009subset, method="sqrt")

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

## remove baseline
s <- removeBaseline(s)

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

## bin peaks
p <- binPeaks(p)

## convert MassPeaks objects into a matrix with missing intensity
## values
intensityMatrix(p)

## convert MassPeaks and MassSpectrum objects into a matrix without
## missing intensity values
intensityMatrix(p, s)

Tests for MassSpectrum or MassPeaks object.

Description

These functions test for a MassSpectrum or MassPeaks object.

Usage

isMassSpectrum(x)

isMassPeaks(x)

Arguments

Value

Returns TRUE or FALSE depending on whether its argument is an MassSpectrum or MassPeaks object.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

MassPeaks, MassSpectrum, AbstractMassObject

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create a MassPeaks object
peaks <- createMassPeaks(mass=1:100, intensity=1:100,
                         metaData=list(name="example 1"))

## test
isMassPeaks(peaks)      # returns TRUE
isMassSpectrum(peaks)   # returns FALSE
isMassPeaks(double())   # returns FALSE

Tests a list of MassSpectrum or MassPeaks objects.

Description

These functions test a list whether containing MassSpectrum or MassSpectrum objects.

Usage

isMassSpectrumList(x)

isMassPeaksList(x)

Arguments

Value

Returns TRUE or FALSE depending on whether its argument is a list of MassSpectrum or MassPeaks objects.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

MassPeaks, MassSpectrum, AbstractMassObject

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create list
p <- list()

## test list
isMassPeaksList(p) # returns FALSE

## create two MassPeaks objects and add them to the list
p <- createMassPeaks(mass=1:100, intensity=1:100,
                     metaData=list(name="example 1"))
p <- createMassPeaks(mass=1:100, intensity=1:100,
                     metaData=list(name="example 2"))

## test list
isMassPeaksList(p)    # returns TRUE
isMassSpectrumList(p) # returns FALSE

Draws peak labels to plot.

Description

labelPeaks draws the corresponding mass values on top of the peaks stored in a MassPeaks object to a plot.

Usage

## S4 method for signature 'MassPeaks'
labelPeaks(object,
  index,
  mass,
  labels,
  digits=3, underline=TRUE,
  verticalOffset=abs(diff(par("usr")[3:4]))*0.01,
  absoluteVerticalPos,
  adj=c(0.5, 0), cex=0.7, srt=0,
  avoidOverlap=FALSE,
  arrowLength=0, arrowLwd=0.5, arrowCol=1,
  ...)

Arguments

Details

Please note that avoidOverlap = TRUE is just supported for srt %% 90 == 0 (means srt has to be a multiple of 90 degree).

Author(s)

Sebastian Gibb

See Also

MassPeaks, plot,AbstractMassObject,missing-method

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create a MassPeaks object from scratch
p <- createMassPeaks(mass=1:20, intensity=sample(x=100:10000, size=20),
                     metaData=list(name="example"))

## plot peaks
plot(p)

## label the first 5 peaks
labelPeaks(p, index=1:5)

## label all peaks in mass range 15 to 20
labelPeaks(p, mass=15:20, underline=FALSE)

## label highest peaks (top 5)
top5 <- intensity(p) %in% sort(intensity(p), decreasing=TRUE)[1:5]
labelPeaks(p, index=top5, col="red")


## real example
data("fiedler2009subset")

## a simplified preprocessing
r <- removeBaseline(fiedler2009subset[[1]])
p <- detectPeaks(r)
plot(p)

## label highest peaks (top 10) and avoid label overlap
top10 <- sort(intensity(p), decreasing=TRUE, index.return=TRUE)$ix[1:10]
labelPeaks(p, index=top10, avoidOverlap=TRUE, digits=1)

## use own labels and rotate by 90 degree
plot(p)
labelPeaks(p, index=top10, labels=paste("TOP", 1:10), underline=FALSE,
           srt=90, adj=c(0, 0.5), col=2)

Relaxed Value Matching

Description

match.closest returns a vector of the positions of (first) matches its first arguments in its second. In contrast to the similar match it just accept numeric arguments but has an additional tolerance argument that allows relaxed matching.

Usage

match.closest(x, table, tolerance = Inf, nomatch = NA_integer_)

Arguments

Value

An integer vector of the same length as x giving the closest position in table of the first match or nomatch if there is no match.

See Also

match

Examples

library("MALDIquant")
match.closest(c(1.1, 1.4, 9.8), 1:10)
# [1]  1  1 10
match.closest(c(1.1, 1.4, 9.8), 1:10, tolerance=0.25)
# [1]  1 NA 10
match.closest(c(1.1, 1.4, 9.8), 1:10, tolerance=0.25, nomatch=0)
# [1]  1  0 10

## this function is most useful if you want to subset an intensityMatrix
## by a few (reference) peaks

## create an example intensityMatrix
im <- matrix(1:10, nrow=2, dimnames=list(NULL, 1:5))
attr(im, "mass") <- 1:5
im
#      1 2 3 4  5
# [1,] 1 3 5 7  9
# [2,] 2 4 6 8 10
# attr(,"mass")
# [1] 1 2 3 4 5

## reference peaks
ref <- c(2.2, 4.8)

im[, match.closest(ref, attr(im, "mass"), tolerance=0.25, nomatch=0)]
#      2  5
# [1,] 3  9
# [2,] 4 10

Merges MassPeaks objects.

Description

This function merges MassPeaks objects.

Usage

mergeMassPeaks(l, labels, method=c("mean", "median", "sum"), ignore.na=TRUE,
               ...)

Arguments

Value

Returns a single (no labels given) or a list (labels given) of merged MassPeaks objects.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

MassPeaks, averageMassSpectra

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create four MassPeaks objects and add them to the list
p <- list(createMassPeaks(mass=1:2, intensity=1:2),
          createMassPeaks(mass=1:3, intensity=1:3),
          createMassPeaks(mass=1:4, intensity=1:4),
          createMassPeaks(mass=1:5, intensity=1:5))

## merge all four MassPeaks objects into a single new one
## by sum their intensities
## (no labels, returns only one new MassPeaks object)
mergedPeaks <- mergeMassPeaks(p, method="sum")

## only merge MassPeaks objects in a group
## (two different labels, returns a list of two new MassPeaks objects)
groups <- factor(c("a", "a", "b", "b"), levels=c("a", "b"))
mergedPeaks <- mergeMassPeaks(p, labels=groups, method="mean")

## the same, but treat NA as zero
mergedPeaks <- mergeMassPeaks(p, labels=groups, method="mean", ignore.na=FALSE)

Finds monoisotopic peaks in a MassPeaks object.

Description

This method looks for monoisotopic peaks in peak list data (represented by a MassPeaks objects).
It is based on the poisson model for isotopic patterns described in Breen et al 2000.

Usage

## S4 method for signature 'MassPeaks'
monoisotopicPeaks(object,
  minCor=0.95, tolerance=1e-4, distance=1.00235, size=3L:10L)
## S4 method for signature 'list'
monoisotopicPeaks(object, ...)

Arguments

Value

Returns a MassPeaks object with monoisotopic peaks only.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

References

K. Park, J.Y. Yoon, S. Lee, E. Paek, H. Park, H.J. Jung, and S.W. Lee. 2008. Isotopic peak intensity ratio based algorithm for determination of isotopic clusters and monoisotopic masses of polypeptides from high-resolution mass spectrometric data. Analytical Chemistry, 80: 7294-7303.

E.J. Breen, F.G. Hopwood, K.L. Williams, and M.R. Wilkins. 2000. Automatic poisson peak harvesting for high throughput protein identification. Electrophoresis 21: 2243-2251.

See Also

MassPeaks, detectPeaks,MassSpectrum-method

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create example peaks
p <- createMassPeaks(mass=995:1005,
                     intensity=c(100, 10, 30, 10, 40, # noise
                                 550, 330, 110, 10,   # isotopic pattern
                                 5, 15))              # more noise
m <- monoisotopicPeaks(p)
as.matrix(m)

## plot the peaks and mark the monoisotopic one
plot(p)
points(m, col=2, pch=4)

Turn a list of AbstractMassObjects into a mass spectrometry imaging slice.

Description

This function turns a mass spectrometry imaging dataset represented by a list of AbstractMassObject objects into an intensityMatrix for each slice (stored in an array).

Usage

msiSlices(x, center, tolerance, method=c("sum", "mean", "median"), adjust=TRUE)

Arguments

Details

Each MassSpectrum/MassPeaks object in x must contain a list named imaging with an element pos that stores the x and y value of the spectrum, e.g.:

> metaData(spectra[[1]])$imaging$pos
x y
1 5

Value

Returns an array of three dimensions. The first and second dimensions contains the x and y coordinates of the image. The third dimension represents the index of the center of each slice. There are two additional attributes, namely "center" and "tolerance" which store the original center and tolerance information.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

AbstractMassObject, MassSpectrum, MassPeaks, coordinates,AbstractMassObject-method, plotMsiSlice,list-method

Please find real examples on:

Website: https://strimmerlab.github.io/software/maldiquant/

Vignette: https://github.com/sgibb/MALDIquantExamples/raw/master/inst/doc/nyakas2013.pdf

Shiny: https://github.com/sgibb/ims-shiny/

Examples

## load package
library("MALDIquant")

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

## please note: this is NOT a MSI data set
## we just add some coordinates for demonstration
coordinates(fiedler2009subset) <- cbind(x=rep(1:4, 2), y=rep(1:2, each=4))

slices <- msiSlices(fiedler2009subset, center=c(5864.49, 8936.97),
                    tolerance=0.25)

slices

Plots an AbstractMassObject object.

Description

This is an overloaded method to allow plotting of an AbstractMassObject object.

Usage

## S4 method for signature 'AbstractMassObject,missing'
plot(x, col="black",
   xlab=expression(italic(m/z)), ylab="intensity",
   type=ifelse(isMassPeaks(x), "h", "l"),
   xlim=c(ifelse(length(x@mass), min(x@mass, na.rm=TRUE), 0),
      ifelse(length(x@mass), max(x@mass, na.rm=TRUE), 1)),
   ylim=c(0, ifelse(length(x@intensity), max(x@intensity, na.rm=TRUE), 1)),
   main=x@metaData$name, sub=x@metaData$file,
   cex.sub=0.75, col.sub="#808080", ...)

Arguments

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

AbstractMassObject

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create a MassSpectrum object by default constructor
s <- createMassSpectrum(mass=1:100, intensity=rnorm(100)^2,
                        metaData=list(name="example"))

## show some details
s

## plot spectrum
plot(s)

Plots a Mass Spectrometry Imaging dataset.

Description

This function allows to plot a slice of a mass spectrometry imaging dataset represented by a list of AbstractMassObject objects or an array or a matrix.

Usage

## S4 method for signature 'list'
plotMsiSlice(x, center, tolerance,
  colRamp=colorRamp(c("black", "blue", "green", "yellow", "red")),
  interpolate=FALSE, legend=TRUE, alignLabels=FALSE, combine=FALSE,
  ...)
## S4 method for signature 'array'
plotMsiSlice(x,
  colRamp=colorRamp(c("black", "blue", "green", "yellow", "red")),
  interpolate=FALSE, legend=TRUE, alignLabels=FALSE, combine=FALSE,
  plotInteractive=FALSE, ...)
## S4 method for signature 'matrix'
plotMsiSlice(x,
  colRamp=colorRamp(c("black", "blue", "green", "yellow", "red")),
  interpolate=FALSE, scale=TRUE, legend=scale, ...)

Arguments

Details

Each MassSpectrum/MassPeaks object in x must contain a list named imaging with an element pos that stores the x and y value of the spectrum, e.g.:

> metaData(spectra[[1]])$imaging$pos
x y
1 5

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

AbstractMassObject, MassSpectrum, MassPeaks, coordinates,AbstractMassObject-method, msiSlices, plot,MassSpectrum,missing-method

Please find real examples on:

Website: https://strimmerlab.github.io/software/maldiquant/

Vignette: https://github.com/sgibb/MALDIquantExamples/raw/master/inst/doc/nyakas2013.pdf

Shiny: https://github.com/sgibb/ims-shiny/

Examples

## load package
library("MALDIquant")

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

## please note: this is NOT a MSI data set
## we just add some coordinates for demonstration
coordinates(fiedler2009subset) <- cbind(x=rep(1:4, 2), y=rep(1:2, each=4))

plotMsiSlice(fiedler2009subset, center=8936.97, tolerance=0.25)

plotMsiSlice(fiedler2009subset, center=c(5864.49, 8936.97), tolerance=0.25,
             combine=TRUE,
             colRamp=list(colorRamp(c("#000000", "#FF00FF")),
                          colorRamp(c("#000000", "#00FF00"))))

Creates a reference MassPeaks object.

Description

This function creates a reference MassPeaks object (also called Anchor Peaks) from a list of MassPeaks objects.
Generally it is a combination of binPeaks and filterPeaks

Usage

referencePeaks(l, method=c("strict", "relaxed"), minFrequency=0.9,
               tolerance=0.002)

Arguments

Value

Returns a new MassPeaks objects.
The intensity slot of the returned MassPeaks represents the frequency of this mass position in all samples.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

binPeaks, filterPeaks, MassPeaks

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create four MassPeaks objects and add them to the list
p<- list(createMassPeaks(mass=1:2, intensity=1:2),
         createMassPeaks(mass=1:3, intensity=1:3),
         createMassPeaks(mass=1:4, intensity=1:4),
         createMassPeaks(mass=1:5, intensity=1:5))

## only use peaks which occur in all MassPeaks objects as reference peaks
refPeaks <- referencePeaks(p, minFrequency=1)

mass(refPeaks)      # 1:2
intensity(refPeaks) # c(1, 1)

Removes the baseline of a MassSpectrum object.

Description

This method removes the baseline of mass spectrometry data (represented by a MassSpectrum object).
The intensity of the mass spectrometry data would be reduced by baseline.

Usage

## S4 method for signature 'MassSpectrum'
removeBaseline(object,
  method=c("SNIP", "TopHat", "ConvexHull", "median"),
  ...)
## S4 method for signature 'list'
removeBaseline(object, ...)

Arguments

Value

Returns a modified MassSpectrum object with reduced intensities.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

MassSpectrum, estimateBaseline,MassSpectrum-method

demo("baseline")

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

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

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

## plot spectrum
plot(s)

## subtract baseline
b <- removeBaseline(s, method="SNIP")

## draw modified spectrum on the plot
lines(b, col="blue")

Smoothes intensities of a MassSpectrum object.

Description

This method smoothes the intensity values of a MassSpectrum object.

Usage

## S4 method for signature 'MassSpectrum'
smoothIntensity(object,
  method=c("SavitzkyGolay", "MovingAverage"), halfWindowSize,
  ...)

Arguments

Details

halfWindowSize: Depends on the selected method. For the SavitzkyGolay the halfWindowSize should be smaller than FWHM of the peaks (full width at half maximum; please find details in Bromba and Ziegler 1981). In general the halfWindowSize for the MovingAverage has to be much smaller than for SavitzkyGolay to conserve the peak shape.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

Weighted moving average: Sigurdur Smarason

References

A. Savitzky and M. J. Golay. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry, 36(8), 1627-1639.

M. U. Bromba and H. Ziegler. 1981. Application hints for Savitzky-Golay digital smoothing filters. Analytical Chemistry, 53(11), 1583-1586.

See Also

MassSpectrum

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

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

## smooth spectra
s <- smoothIntensity(fiedler2009subset, method="MovingAverage",
                     halfWindowSize=2)
## or
s <- smoothIntensity(fiedler2009subset, method="MovingAverage",
                     halfWindowSize=2, weighted=TRUE)
## or
s <- smoothIntensity(fiedler2009subset, method="SavitzkyGolay",
                     halfWindowSize=10)

Transforms intensities of an AbstractMassObject object.

Description

This method performs a transformation (e.g. sqrt-transformation) on the intensities of an AbstractMassObject object.

Usage

## S4 method for signature 'AbstractMassObject'
transformIntensity(object,
  method=c("sqrt", "log", "log2", "log10"))
## S4 method for signature 'list'
transformIntensity(object, ...)

Arguments

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

AbstractMassObject, MassSpectrum

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

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

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

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

## plot spectrum
par(mfrow=c(2, 1))
plot(s, main="raw spectrum")
plot(t, main="transformed spectrum")
par(mfrow=c(1, 1))

Trim an AbstractMassObject object.

Description

This method trims an AbstractMassObject object. That is useful if some mass ranges should be excluded from further analysis.

Usage

## S4 method for signature 'AbstractMassObject,numeric'
trim(object, range)
## S4 method for signature 'list,numeric'
trim(object, range, ...)
## S4 method for signature 'list,missing'
trim(object, range, ...)

Arguments

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

AbstractMassObject, MassPeaks, MassSpectrum

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

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

## select only one spectrum
s <- fiedler2009subset[[1]]

## remove all mass lower 3000
trim(s, range=c(3000, Inf))

## remove all mass higher 8000
trim(s, range=c(0, 8000))

## remove all mass lower 3000 and higher 8000
trim(s, range=c(3000, 8000))

## choose largest overlapping mass range for all spectra
trim(fiedler2009subset)

Run warping functions on AbstractMassObject objects.

Description

These functions run warping functions on AbstractMassObject objects (warping is also known as phase correction).

Usage

warpMassPeaks(l, w, emptyNoMatches=FALSE)

warpMassSpectra(l, w, emptyNoMatches=FALSE)

Arguments

Details

The warping function w is called in the following way:

newMass = oldMass + w(oldMass)

Value

Returns a list of warped MassPeaks or MassSpectrum objects.

Author(s)

Sebastian Gibb mail@sebastiangibb.de

See Also

determineWarpingFunctions, MassPeaks, MassSpectrum

Website: https://strimmerlab.github.io/software/maldiquant/

Examples

## load package
library("MALDIquant")

## create a MassPeaks object
p <- createMassPeaks(mass=1:5, intensity=1:5)

## stupid warping function for demonstration
## (please use determineWarpingFunctions in real life applications)
simpleWarp <- function(x) { return(1) }

## run warping function
w <- warpMassPeaks(list(p), list(simpleWarp))[[1]]

## compare results
all(mass(w) == mass(p)+1) # TRUE

## no warping (MassPeaks object is not changed)
warpMassPeaks(list(p), list(NA))

## no warping (intensity values of MassPeaks object are set to zero)
warpMassPeaks(list(p), list(NA), emptyNoMatches=TRUE)