Entropy similarity between two MS/MS spectra — spec_entropy

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. 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 there GitHub.

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"