msentropy_similarity {msentropy}

Calculate spectral entropy similarity between two spectra

Description

msentropy_similarity calculates the spectral entropy between two spectra (Li et al. 2021). It is a wrapper function defining defaults for parameters and calling the calculate_entropy_similarity() or calculate_unweighted_entropy_similarity() functions to perform the calculation.

Usage

msentropy_similarity(
  peaks_a,
  peaks_b,
  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

peaks_a

A two-column numeric matrix with the m/z and intensity values for peaks of one spectrum.

peaks_b

A two-column numeric matrix with the m/z and intensity values for peaks of one spectrum.

ms2_tolerance_in_da

The MS2 tolerance in Da, set to -1 to disable. Defaults to ms2_tolerance_in_da = 0.02.

ms2_tolerance_in_ppm

The MS2 tolerance in ppm, set to -1 to disable. Defaults to ms2_tolerance_in_ppm = -1.

clean_spectra

Whether to clean the spectra before calculating the entropy similarity, see clean_spectrum().

min_mz

The minimum mz value to keep, set to -1 to disable. Defaults to min_mz = 0.

max_mz

The maximum mz value to keep, set to -1 to disable. Defaults to max_mz = 1000.

noise_threshold

The noise threshold, set to -1 to disable, all peaks have intensity < noise_threshold * max_intensity will be removed. Defaults to noise_threshold = 0.01, thus, by default, all peaks with an intensity less than 1% of the maximum intensity of a spectrum will be removed.

max_peak_num

The maximum number of peaks to keep, set to -1 to disable. Defaults to max_peak_num = 1000.

weighted

logical(1) whether the weighted or unweighted entropy similarity should be calculated. Defaults to weighted = TRUE, thus calculate_entropy_similarity() is used for the calculation. For weighted = FALSE calculate_unweighted_entropy_similarity() is used instead.

...

Optional additional parameters (currently ignored)

Value

The entropy similarity

References

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

Examples

peaks_a <- cbind(mz = c(169.071, 186.066, 186.0769),
    intensity = c(7.917962, 1.021589, 100.0))
peaks_b <- cbind(mz = c(120.212, 169.071, 186.066),
    intensity <- c(37.16, 66.83, 999.0))
msentropy_similarity(peaks_a, peaks_b, ms2_tolerance_in_da = 0.02)