Calculate Neutral Loss Spectra

This help page lists functions that convert MS/MS spectra to neutral loss spectra. The main function for this is neutralLoss and the specific algorithm to be used is defined (and configured) with dedicated parameter objects (paramer param of the neutralLoss() function).

The parameter objects for the different algorithms are:

• PrecursorMzParam(): calculates neutral loss spectra as in Aisporna et al. 2022 by subtracting the (fragment's) peak m/z value from the precursor m/z value of each spectrum (precursor m/z - fragment m/z). Parameter msLevel allows to restrict calculation of neutral loss spectra to specified MS level(s). Spectra from other MS level(s) are returned as-is. Parameter filterPeaks allows to remove certain peaks from the neutral loss spectra. By default (filterPeaks = "none") no filtering takes place. With filterPeaks = "removePrecursor" all fragment peaks with an m/z value matching the precursor m/z (considering also ppm and tolerance are removed. With filterPeaks = "abovePrecursor", all fragment peaks with an m/z larger than the precursor m/z (m/z > precursor m/z - tolerance - ppm of the precursor m/z) are removed (thus removing also in most cases the fragment peaks representing the precursor). Finally, with filterPeaks = "belowPrecursor" all fragment peaks with an m/z smaller than the precursor m/z (m/z < precursor m/z + tolerance + ppm of the precursor m/z) are removed. Also in this case the precursor fragment peak is (depending on the values of ppm and tolerance) removed.

Usage

PrecursorMzParam(
  filterPeaks = c("none", "abovePrecursor", "belowPrecursor", "removePrecursor"),
  msLevel = c(2L, NA_integer_),
  ppm = 10,
  tolerance = 0
)

# S4 method for class 'Spectra,PrecursorMzParam'
neutralLoss(object, param, ...)

Arguments

filterPeaks

For PrecursorMzParam(): character(1) or function defining if and how fragment peaks should be filtered before calculation. Pre-defined options are: "none" (keep all peaks), "abovePrecursor" (removes all fragment peaks with an m/z >= precursor m/z), "belowPrecursor" (removes all fragment peaks with an m/z <= precursor m/z). In addition, it is possible to pass a custom function with this parameter with arguments x (two column peak matrix) and precursorMz (the precursor m/z) that returns the sub-setted two column peak matrix.

msLevel

integer defining for which MS level(s) the neutral loss spectra should be calculated. Defaults to msLevel = c(2L, NA) thus, neutral loss spectra will be calculated for all spectra with MS level equal to 2 or with missing/undefined MS level. All spectra with a MS level different than msLevel will be returned unchanged.

ppm

numeric(1) with m/z-relative acceptable difference in m/z values to filter peaks. Defaults to ppm = 10. See function description for details.

tolerance

numeric(1) with absolute acceptable difference in m/z values to filter peaks. Defaults to tolerance = 0. See function description for details.

object

Spectra() object with the fragment spectra for which neutral loss spectra should be calculated.

param

One of the parameter objects discussed below.

...

Currently ignored.

Value

A Spectra() object with calculated neutral loss spectra.

Note

By definition, mass peaks in a Spectra object need to be ordered by their m/z value (in increasing order). Thus, the order of the peaks in the calculated neutral loss spectra might not be the same than in the original Spectra object.

Note also that for spectra with a missing precursor m/z empty spectra are returned (i.e. spectra without peaks) since it is not possible to calcualte the neutral loss spectra.

References

Aisporna A, Benton PH, Chen A, Derks RJE, Galano JM, Giera M and Siuzdak G (2022). Neutral Loss Mass Spectral Data Enhances Molecular Similarity Analysis in METLIN. Journal of the American Society for Mass Spectrometry. doi:10.1021/jasms.1c00343

See also

addProcessing() for other data analysis and manipulation functions.

Author

Johannes Rainer

Examples

## Create a simple example Spectra object with some MS1, MS2 and MS3 spectra.
DF <- DataFrame(msLevel = c(1L, 2L, 3L, 1L, 2L, 3L),
                precursorMz = c(NA, 40, 20, NA, 300, 200))
DF$mz <- IRanges::NumericList(
                      c(3, 12, 14, 15, 16, 200),
                      c(13, 23, 39, 86),
                      c(5, 7, 20, 34, 50),
                      c(5, 7, 9, 20, 100),
                      c(15, 53, 299, 300),
                      c(34, 56, 100, 200, 204, 309)
                  , compress = FALSE)
DF$intensity <- IRanges::NumericList(1:6, 1:4, 1:5, 1:5, 1:4, 1:6,
                                     compress = FALSE)
sps <- Spectra(DF, backend = MsBackendDataFrame())

## Calculate neutral loss spectra for all MS2 spectra, keeping MS1 and MS3
## spectra unchanged.
sps_nl <- neutralLoss(sps, PrecursorMzParam(msLevel = 2L))
mz(sps)
#> NumericList of length 6
#> [[1]] 3 12 14 15 16 200
#> [[2]] 13 23 39 86
#> [[3]] 5 7 20 34 50
#> [[4]] 5 7 9 20 100
#> [[5]] 15 53 299 300
#> [[6]] 34 56 100 200 204 309
mz(sps_nl)
#> NumericList of length 6
#> [[1]] 3 12 14 15 16 200
#> [[2]] -46 1 17 27
#> [[3]] 5 7 20 34 50
#> [[4]] 5 7 9 20 100
#> [[5]] 0 1 247 285
#> [[6]] 34 56 100 200 204 309

## Calculate neutral loss spectra for MS2 and MS3 spectra, removing peaks
## with an m/z >= precursorMz
sps_nl <- neutralLoss(sps, PrecursorMzParam(
    filterPeaks = "abovePrecursor", msLevel = 2:3))
mz(sps_nl)
#> NumericList of length 6
#> [[1]] 3 12 14 15 16 200
#> [[2]] 1 17 27
#> [[3]] 13 15
#> [[4]] 5 7 9 20 100
#> [[5]] 1 247 285
#> [[6]] 100 144 166
## This removed also the peak with m/z 39 from the second spectrum

## Removing all fragment peaks matching the precursor m/z with a tolerance
## of 1 and ppm 10
sps_nl <- neutralLoss(sps, PrecursorMzParam(
    filterPeaks = "removePrecursor", tolerance = 1, ppm = 10, msLevel = 2:3))
mz(sps_nl)
#> NumericList of length 6
#> [[1]] 3 12 14 15 16 200
#> [[2]] -46 17 27
#> [[3]] -30 -14 13 15
#> [[4]] 5 7 9 20 100
#> [[5]] 247 285
#> [[6]] -109 -4 100 144 166

## Empty spectra are returned for MS 2 spectra with undefined precursor m/z.
sps$precursorMz <- NA_real_
sps_nl <- neutralLoss(sps, PrecursorMzParam())
mz(sps_nl)
#> NumericList of length 6
#> [[1]] 3 12 14 15 16 200
#> [[2]] numeric(0)
#> [[3]] 5 7 20 34 50
#> [[4]] 5 7 9 20 100
#> [[5]] numeric(0)
#> [[6]] 34 56 100 200 204 309