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Type: Package
Title: Spectral Entropy for Mass Spectrometry Data
Version: 0.1.4
Date: 2023-08-07
Description: Clean the MS/MS spectrum, calculate spectral entropy, unweighted entropy similarity, and entropy similarity for mass spectrometry data. The entropy similarity is a novel similarity measure for MS/MS spectra which outperform the widely used dot product similarity in compound identification. For more details, please refer to the paper: Yuanyue Li et al. (2021) "Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification" <doi:10.1038/s41592-021-01331-z>.
License: Apache License (== 2.0)
Depends: R (≥ 3.5.0), Rcpp (≥ 1.0.10)
Suggests: testthat
LinkingTo: Rcpp
RoxygenNote: 7.2.3
Encoding: UTF-8
URL: https://github.com/YuanyueLi/MSEntropy
NeedsCompilation: yes
Packaged: 2023-08-07 22:58:36 UTC; yli
Author: Yuanyue Li [aut, cre]
Maintainer: Yuanyue Li <liyuanyue@gmail.com>
Repository: CRAN
Date/Publication: 2023-08-07 23:10:02 UTC

Entropy similarity between two spectra

Description

Calculate the entropy similarity between two spectra

Usage

calculate_entropy_similarity(
  peaks_a,
  peaks_b,
  ms2_tolerance_in_da,
  ms2_tolerance_in_ppm,
  clean_spectra,
  min_mz,
  max_mz,
  noise_threshold,
  max_peak_num
)

Arguments

peaks_a

A matrix of spectral peaks, with two columns: mz and intensity

peaks_b

A matrix of spectral peaks, with two columns: mz and intensity

ms2_tolerance_in_da

The MS2 tolerance in Da, set to -1 to disable

ms2_tolerance_in_ppm

The MS2 tolerance in ppm, set to -1 to disable

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

max_mz

The maximum mz value to keep, set to -1 to disable

noise_threshold

The noise threshold, set to -1 to disable, all peaks have intensity < noise_threshold * max_intensity will be removed

max_peak_num

The maximum number of peaks to keep, set to -1 to disable

Value

The entropy similarity

Examples

mz_a <- c(169.071, 186.066, 186.0769)
intensity_a <- c(7.917962, 1.021589, 100.0)
mz_b <- c(120.212, 169.071, 186.066)
intensity_b <- c(37.16, 66.83, 999.0)
peaks_a <- matrix(c(mz_a, intensity_a), ncol = 2, byrow = FALSE)
peaks_b <- matrix(c(mz_b, intensity_b), ncol = 2, byrow = FALSE)
calculate_entropy_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)

Calculate spectral entropy of a spectrum

Description

Calculate spectral entropy of a spectrum

Usage

calculate_spectral_entropy(peaks)

Arguments

peaks

A matrix of peaks, with two columns: m/z and intensity.

Value

A double value of spectral entropy.

Examples

mz <- c(100.212, 300.321, 535.325)
intensity <- c(37.16, 66.83, 999.0)
peaks <- matrix(c(mz, intensity), ncol = 2, byrow = FALSE)
calculate_spectral_entropy(peaks)

Unweighted entropy similarity between two spectra

Description

Calculate the unweighted entropy similarity between two spectra

Usage

calculate_unweighted_entropy_similarity(
  peaks_a,
  peaks_b,
  ms2_tolerance_in_da,
  ms2_tolerance_in_ppm,
  clean_spectra,
  min_mz,
  max_mz,
  noise_threshold,
  max_peak_num
)

Arguments

peaks_a

A matrix of spectral peaks, with two columns: mz and intensity

peaks_b

A matrix of spectral peaks, with two columns: mz and intensity

ms2_tolerance_in_da

The MS2 tolerance in Da, set to -1 to disable

ms2_tolerance_in_ppm

The MS2 tolerance in ppm, set to -1 to disable

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

max_mz

The maximum mz value to keep, set to -1 to disable

noise_threshold

The noise threshold, set to -1 to disable, all peaks have intensity < noise_threshold * max_intensity will be removed

max_peak_num

The maximum number of peaks to keep, set to -1 to disable

Value

The unweighted entropy similarity

Examples

mz_a <- c(169.071, 186.066, 186.0769)
intensity_a <- c(7.917962, 1.021589, 100.0)
mz_b <- c(120.212, 169.071, 186.066)
intensity_b <- c(37.16, 66.83, 999.0)
peaks_a <- matrix(c(mz_a, intensity_a), ncol = 2, byrow = FALSE)
peaks_b <- matrix(c(mz_b, intensity_b), ncol = 2, byrow = FALSE)
calculate_unweighted_entropy_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)

Clean a spectrum

Description

Clean a spectrum

This function will clean the peaks by the following steps: 1. Remove empty peaks (mz <= 0 or intensity <= 0). 2. Remove peaks with mz >= max_mz or mz < min_mz. 3. Centroid the spectrum by merging peaks within min_ms2_difference_in_da or min_ms2_difference_in_ppm. 4. Remove peaks with intensity < noise_threshold * max_intensity. 5. Keep only the top max_peak_num peaks. 6. Normalize the intensity to sum to 1.

Note: The only one of min_ms2_difference_in_da and min_ms2_difference_in_ppm should be positive.

Usage

clean_spectrum(
  peaks,
  min_mz,
  max_mz,
  noise_threshold,
  min_ms2_difference_in_da,
  min_ms2_difference_in_ppm,
  max_peak_num,
  normalize_intensity
)

Arguments

peaks

A matrix of spectral peaks, with two columns: mz and intensity

min_mz

The minimum mz value to keep, set to -1 to disable

max_mz

The maximum mz value to keep, set to -1 to disable

noise_threshold

The noise threshold, set to -1 to disable, all peaks have intensity < noise_threshold * max_intensity will be removed

min_ms2_difference_in_da

The minimum mz difference in Da to merge peaks, set to -1 to disable, any two peaks with mz difference < min_ms2_difference_in_da will be merged

min_ms2_difference_in_ppm

The minimum mz difference in ppm to merge peaks, set to -1 to disable, any two peaks with mz difference < min_ms2_difference_in_ppm will be merged

max_peak_num

The maximum number of peaks to keep, set to -1 to disable

normalize_intensity

Whether to normalize the intensity to sum to 1

Value

A matrix of spectral peaks, with two columns: mz and intensity

Examples

mz <- c(100.212, 169.071, 169.078, 300.321)
intensity <- c(0.3716, 7.917962, 100., 66.83)
peaks <- matrix(c(mz, intensity), ncol = 2, byrow = FALSE)
clean_spectrum(peaks, min_mz = 0, max_mz = 1000, noise_threshold = 0.01,
               min_ms2_difference_in_da = 0.02, min_ms2_difference_in_ppm = -1,
               max_peak_num = 100, normalize_intensity = TRUE)

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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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