R: Combine spectra to a consensus spectrum

consensusSpectrum {MSnbase}

R Documentation

Combine spectra to a consensus spectrum

Description

consensusSpectrum takes a list of spectra and combines them to a consensus spectrum containing mass peaks that are present in a user definable proportion of spectra.

Usage

consensusSpectrum(x, mzd, minProp = 0.5, intensityFun = base::max,
  ppm = 0, ...)

Arguments

`x`	`list` of Spectrum objects (either Spectrum1 or Spectrum2).
`mzd`	`numeric(1)` defining the maximal m/z difference below which mass peaks are grouped in to the same final mass peak (see details for more information). If not provided this value is estimated from the distribution of differences of m/z values from the spectra (see `meanMzInts()` for more details). See also parameter `ppm` below for the definition of an m/z dependent peak grouping.
`minProp`	`numeric(1)` defining the minimal proportion of spectra in which a mass peak has to be present in order to include it in the final consensus spectrum. Should be a number between 0 and 1 (present in all spectra).
`intensityFun`	`function` to be used to define the intensity of the aggregated peak. By default the maximum signal for a mass peak is reported.
`ppm`	`numeric(1)` allowing to perform a m/z dependent grouping of mass peaks. See details for more information.
`...`	additional arguments to be passed to `intensityFun`.

Details

Peaks from spectra with a difference of their m/z being smaller than mzd are grouped into the same final mass peak with their intensities being aggregated with intensityFun. The m/z of the final mass peaks is calculated using a intensity-weighted mean of the m/z values from the individual mass peaks. Alternatively (or in addition) it is possible to perform an m/z dependent grouping of mass peaks with parameter ppm: mass peaks from different spectra with a difference in their m/z smaller than ppm of their m/z are grouped into the same final peak.

Author(s)

Johannes Rainer

Examples

library(MSnbase)
## Create 3 example spectra.
sp1 <- new("Spectrum2", rt = 1, precursorMz = 1.41,
    mz = c(1.2, 1.5, 1.8, 3.6, 4.9, 5.0, 7.8, 8.4),
    intensity = c(10, 3, 140, 14, 299, 12, 49, 20))
sp2 <- new("Spectrum2", rt = 1.1, precursorMz = 1.4102,
    mz = c(1.4, 1.81, 2.4, 4.91, 6.0, 7.2, 9),
    intensity = c(3, 184, 8, 156, 12, 23, 10))
sp3 <- new("Spectrum2", rt = 1.2, precursorMz = 1.409,
    mz = c(1, 1.82, 2.2, 3, 7.0, 8),
    intensity = c(8, 210, 7, 101, 17, 8))
spl <- Spectra(sp1, sp2, sp3)

## Plot the spectra, each in a different color
par(mfrow = c(2, 1), mar = c(4.3, 4, 1, 1))
plot(mz(sp1), intensity(sp1), type = "h", col = "#ff000080", lwd = 2,
    xlab = "m/z", ylab = "intensity", xlim = range(mz(spl)),
    ylim = range(intensity(spl)))
points(mz(sp2), intensity(sp2), type = "h", col = "#00ff0080", lwd = 2)
points(mz(sp3), intensity(sp3), type = "h", col = "#0000ff80", lwd = 2)

cons <- consensusSpectrum(spl, mzd = 0.02, minProp = 2/3)

## Peaks of the consensus spectrum
mz(cons)
intensity(cons)

## Other Spectrum data is taken from the first Spectrum in the list
rtime(cons)
precursorMz(cons)

plot(mz(cons), intensity(cons), type = "h", xlab = "m/z", ylab = "intensity",
    xlim = range(mz(spl)), ylim = range(intensity(spl)), lwd = 2)

[Package MSnbase version 2.8.3 Index]