MSnbase: estimateMzScattering – R documentation

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estimateMzScattering

Estimate m/z scattering in consecutive scans

Description

Estimate scattering of m/z values (due to technical, instrument specific noise) for the same ion in consecutive scans of a LCMS experiment.

Usage

estimateMzScattering(x, halfWindowSize = 1L, timeDomain = FALSE)

Arguments

`x`	`MSnExp` or `OnDiskMSnExp` object.
`halfWindowSize`	`integer(1)` defining the half window size for the moving window to combine consecutive spectra.
`timeDomain`	`logical(1)` whether m/z scattering should be estimated on `mz` (`timeDomain = FALSE`) or `sqrt(mz)` (`timeDomain = TRUE`) values. See `combineSpectraMovingWindow()` for details on this parameter.

Details

The m/z values of the same ions in consecutive scans (spectra) of a LCMS run will not be identical. This random noise is expected to be smaller than the resolution of the MS instrument. The distribution of differences of m/z values from neighboring spectra is thus expected to be (at least) bi-modal with the first peak representing the above described random variation and the second (or largest) peak the m/z resolution. The m/z value of the first local minimum between these first two peaks in the distribution is returned as the m/z scattering.

Note

For timeDomain = TRUE the function does not return the estimated scattering of m/z values, but the scattering of sqrt(mz) values.

Author(s)

Johannes Rainer

Examples

library(MSnbase)
library(msdata)
## Load a profile-mode LC-MS data file
f <- dir(system.file("sciex", package = "msdata"), full.names = TRUE)[1]
im <- readMSData(f, mode = "inMem", msLevel = 1L)

res <- estimateMzScattering(im)

## Plot the distribution of estimated m/z scattering
plot(density(unlist(res)))

## Compare the m/z resolution and m/z scattering of the spectrum with the
## most peaks
idx <- which.max(unlist(spectrapply(im, peaksCount)))

res[[idx]]
abline(v = res[[idx]], lty = 2)
estimateMzResolution(im[[idx]])
## As expected, the m/z scattering is much lower than the m/z resolution.

MSnbase

Base Functions and Classes for Mass Spectrometry and Proteomics

v2.16.1

Artistic-2.0

Authors

Laurent Gatto, Johannes Rainer and Sebastian Gibb with contributions from Guangchuang Yu, Samuel Wieczorek, Vasile-Cosmin Lazar, Vladislav Petyuk, Thomas Naake, Richie Cotton, Arne Smits, Martina Fisher, Ludger Goeminne, Adriaan Sticker and Lieven Clement.

Initial release