MALDIquant (version 1.22.3)

determineWarpingFunctions: 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, ...)

Value

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

Arguments

l

list, list of MassPeaks objects.

reference

MassPeaks, reference object to which the samples (l) should be aligned. If missing referencePeaks is used.

tolerance

double, maximal relative deviation of a peak position (mass) to be considered as identical. Must be multiplied by 10^-6 for ppm, e.g. use tolerance=5e-6 for 5 ppm.

method

used basic warping function.

allowNoMatches

logical, don't throw an error if an MassPeaks object could not match to the reference.

plot

logical, if TRUE a warping plot is drawn for each sample.

plotInteractive

logical, if FALSE a non-interactive device (e.g. pdf) is used for warping plots.

...

arguments to be passed to warpingFunction

Author

Sebastian Gibb

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()
  

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))