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GNPS spectrum similarity scores

Source: R/gnps.R
gnps.Rd

The join_gnps and gnps functions allow to calculate spectra similarity scores as used in GNPS. The approach matches first peaks between the two spectra directly using a user-defined ppm and/or tolerance as well as using a fixed delta m/z (considering the same ppm and tolerance) that is defined by the difference of the two spectras' precursor m/z values. For peaks that match multiple peaks in the other spectrum only the matching peak pair with the higher value/similarity is considered in the final similarity score calculation. Note that GNPS similarity scores are calculated only if the two functions are used together.

  • join_gnps: matches/maps peaks between spectra with the same approach as in GNPS: peaks are considered matching if a) the difference in their m/z values is smaller than defined by tolerance and ppm (this is the same as joinPeaks) and b) the difference of their m/z adjusted for the difference of the spectras' precursor is smaller than defined by tolerance and ppm. Based on this definition, peaks in x can match up to two peaks in y hence returned peak indices might be duplicated. Note that if one of xPrecursorMz or yPrecursorMz are NA or if both are the same, the results are the same as with join(). The function returns a list of two integer vectors with the indices of the peaks matching peaks in the other spectrum or NA otherwise.

  • gnps: calculates the GNPS similarity score on peak matrices' previously aligned (matched) with join_gnps. For multi-mapping peaks the pair with the higher similarity are considered in the final score calculation.

Usage

gnps(x, y, ...)

join_gnps(
  x,
  y,
  xPrecursorMz = NA_real_,
  yPrecursorMz = NA_real_,
  tolerance = 0,
  ppm = 0,
  type = "outer",
  ...
)

Arguments

x

for join_gnps: numeric with m/z values from a spectrum. For gnps: matrix with two columns "mz" and "intensity" containing the peaks aligned with peaks in y (with join_gnps).

y

for join_gnps: numeric with m/z values from a spectrum. For gnps: matrix with two columns "mz" and "intensity" containing the peaks aligned with peaks in x (with join_gnps).

...

for join_gnps: optional parameters passed to the join() function. For gnps: ignored.

xPrecursorMz

for join_gnps: numeric(1) with the precursor m/z of the spectrum x.

yPrecursorMz

for join_gnps: numeric(1) with the precursor m/z of the spectrum y.

tolerance

for join_gnps: numeric(1) defining a constant maximal accepted difference between m/z values of peaks from the two spectra to be matched/mapped.

ppm

for join_gnps: numeric(1) defining a relative, m/z-dependent, maximal accepted difference between m/z values of peaks from the two spectra to be matched/mapped.

type

for join_gnps: character(1) specifying the type of join that should be performed. See join() for details and options. Defaults to type = "outer".

Value

See function definition in the description section.

Details

The implementation of gnps bases on the R code from the publication listed in the references.

References

Xing S, Hu Y, Yin Z, Liu M, Tang X, Fang M, Huan T. Retrieving and Utilizing Hypothetical Neutral Losses from Tandem Mass Spectra for Spectral Similarity Analysis and Unknown Metabolite Annotation. Anal Chem. 2020 Nov 3;92(21):14476-14483. doi:10.1021/acs.analchem.0c02521 .

See also

Other grouping/matching functions: bin(), closest()

Other distance/similarity functions: distance

Author

Johannes Rainer, Michael Witting, based on the code from Xing et al. (2020).

Examples


## Define spectra
x <- cbind(mz = c(10, 36, 63, 91, 93), intensity = c(14, 15, 999, 650, 1))
y <- cbind(mz = c(10, 12, 50, 63, 105), intensity = c(35, 5, 16, 999, 450))
## The precursor m/z
pmz_x <- 91
pmz_y <- 105

## Plain join identifies only 2 matching peaks
join(x[, 1], y[, 1])
#> $x
#> [1]  1 NA  2 NA  3  4  5 NA
#> 
#> $y
#> [1]  1  2 NA  3  4 NA NA  5
#> 

## join_gnps finds 4 matches
join_gnps(x[, 1], y[, 1], pmz_x, pmz_y)
#> $x
#>  [1]  1  2  2  3  4  4  5 NA NA NA
#> 
#> $y
#>  [1]  1 NA  3  4 NA  5 NA  2  3  5
#> 

## with one of the two precursor m/z being NA, the result are the same as
## with join.
join_gnps(x[, 1], y[, 1], pmz_x, yPrecursorMz = NA)
#> $x
#> [1]  1 NA  2 NA  3  4  5 NA
#> 
#> $y
#> [1]  1  2 NA  3  4 NA NA  5
#> 

## Calculate GNPS similarity score:
map <- join_gnps(x[, 1], y[, 1], pmz_x, pmz_y)
gnps(x[map[[1]], ], y[map[[2]], ])
#> [1] 0.9923501