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Calculate spectral entropy similarity between two MS2 spectra

Usage

spec_entropy_params(
  ms2_tolerance_in_da = 0.02,
  ms2_tolerance_in_ppm = -1,
  clean_spectra = TRUE,
  min_mz = 0,
  max_mz = 1000,
  noise_threshold = 0.01,
  max_peak_num = 100,
  weighted = TRUE
)

Arguments

ms2_tolerance_in_da

MS2 peak tolerance in Da, set to -1 to disable. Defaults to 0.02.

ms2_tolerance_in_ppm

MS2 peak tolerance in ppm, set to -1 to disable. Defaults to -1.

clean_spectra

Either TRUE or FALSE to clean the spectra prior to calculating similarity. See msentropy::clean_spectrum for more information. Defaults to TRUE.

min_mz

numeric, minimum mz to keep, set to -1 to disable. Defaults to 0.

max_mz

numeric, maximum mz to keep, set to -1 to disable. Defaults to 1000.

noise_threshold

Background intensity threshold, all peaks with intensity < noise_threshold * max_intensity are removed. Set to -1 to disable. Defaults to 0.01.

max_peak_num

numeric, maximum number of peaks to keep for score calculation. Set to -1 to disable. Defaults to 100.

weighted

logical whether weighted or unweighted entropy similarity will be calculated. Defaults to TRUE.

Value

A parameters list for similarity scoring method "spectral_entropy"

Details

spec_entropy_params() will initiate spectral entropy similarity scoring via the msentropy package (Li et al. 2021). For more information about parameters, see msentropy::msentropy_similarity().

References

Li, Y., Kind, T., Folz, J. et al. Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification, Nat Methods 18, 1524–1531 (2021). https://doi.org/10.1038/s41592-021-01331-z

Examples

spec_entropy_params()
#> $ms2_tolerance_in_da
#> [1] 0.02
#> 
#> $ms2_tolerance_in_ppm
#> [1] -1
#> 
#> $clean_spectra
#> [1] TRUE
#> 
#> $min_mz
#> [1] 0
#> 
#> $max_mz
#> [1] 1000
#> 
#> $noise_threshold
#> [1] 0.01
#> 
#> $max_peak_num
#> [1] 100
#> 
#> $weighted
#> [1] TRUE
#> 
#> $method
#> [1] "entropy"
#> 
#> attr(,"class")
#> [1] "parameters"