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
TRUEorFALSEto clean the spectra prior to calculating similarity. Seemsentropy::clean_spectrumfor more information. Defaults toTRUE.- min_mz
numeric, minimum mz to keep, set to -1 to disable. Defaults to0.- max_mz
numeric, maximum mz to keep, set to -1 to disable. Defaults to1000.- 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 to100.- weighted
logicalwhether weighted or unweighted entropy similarity will be calculated. Defaults toTRUE.
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"