If you have any questions, feel free to contact me via email: yuanyueli@zju.edu.cn. If you find this package useful, please consider citing the following publications:
Li, Y., Fiehn, O. Flash entropy search to query all mass spectral libraries in real time, Nat Methods 20, 1475-1478 (2023). https://doi.org/10.1038/s41592-023-02012-9
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
Theoretical Background
Spectral entropy is an useful property to measure the complexity of a spectrum. It is inspried by the concept of Shannon entropy in information theory. (ref)
Entropy similarity, which measured spectral similarity based on spectral entropy, has been shown to outperform dot product similarity in compound identification. (ref)
The Flash Entropy Search algorithm significantly accelerates the computation of entropy similarity (ref)
How to use this package
This repository provides the source code for calculating spectral entropy and entropy similarity in multiple programming languages. The Flash Entropy Search algorithm is also implemented in Python.
For Python users
A detailed tutorial is available here: https://msentropy.readthedocs.io
Installation
pip install ms_entropy
Usage of Classical entropy functions
import numpy as np
import ms_entropy as me
peaks_query = np.array([[69.071, 7.917962], [86.066, 1.021589], [86.0969, 100.0]], dtype = np.float32)
peaks_reference = np.array([[41.04, 37.16], [69.07, 66.83], [86.1, 999.0]], dtype = np.float32)
# Calculate entropy similarity.
entropy = me.calculate_spectral_entropy(peaks_query, clean_spectrum = True, min_ms2_difference_in_da = 0.05)
print(f"Spectral entropy is {entropy}.")
# Calculate unweighted entropy similarity.
unweighted_similarity = me.calculate_unweighted_entropy_similarity(peaks_query, peaks_reference, ms2_tolerance_in_da = 0.05)
print(f"Unweighted entropy similarity: {unweighted_similarity}.")
# Calculate entropy similarity.
similarity = me.calculate_entropy_similarity(peaks_query, peaks_reference, ms2_tolerance_in_da = 0.05)
print(f"Entropy similarity: {similarity}.")
Usage of Flash Entropy Search
from ms_entropy import FlashEntropySearch
entropy_search = FlashEntropySearch()
entropy_search.build_index(spectral_library)
entropy_similarity = entropy_search.search(
precursor_mz=query_spectrum_precursor_mz, peaks=query_spectrum_peaks)
For R users
Documentation is available at: https://cran.r-project.org/web/packages/msentropy/msentropy.pdf
Installation
install.packages("msentropy")
Usage
library(msentropy)
# Peaks A
mz_a <- c(169.071, 186.066, 186.0769)
intensity_a <- c(7.917962, 1.021589, 100.0)
peaks_a <- matrix(c(mz_a, intensity_a), ncol = 2, byrow = FALSE)
# Peaks B
mz_b <- c(120.212, 169.071, 186.066)
intensity_b <- c(37.16, 66.83, 999.0)
peaks_b <- matrix(c(mz_b, intensity_b), ncol = 2, byrow = FALSE)
# Calculate spectral entropy
spectral_entropy_a <- calculate_spectral_entropy(clean_spectrum(peaks_a, min_ms2_difference_in_da = 0.02))
spectral_entropy_b <- calculate_spectral_entropy(clean_spectrum(peaks_b, min_ms2_difference_in_da = 0.02))
# Calculate entropy similarity
entropy_similarity <- calculate_entropy_similarity(peaks_a, peaks_b, ms2_tolerance_in_da = 0.02)
For C/C++ users
Usage
#include "SpectralEntropy.h"
// Calculate spectral entropy
{
int spec_a_len = 3;
float spec_a[3][2] = {{169.071, 7.917962}, {186.066, 1.021589}, {186.0769, 100.0}};
// The parameters for clean_spectrum function
int normalize_intensity = 1;
float ms2_tolerance_in_da = 0.02, ms2_tolerance_in_ppm = -1;
float min_mz= 0, max_mz = -1;
float noise_threshold = 0.01;
int max_peak_num = -1;
// Always clean the spectrum before calculating spectral entropy
spec_a_len = clean_spectrum(*spec_a, spec_a_len, min_mz, max_mz, noise_threshold, max_peak_num, ms2_tolerance_in_da, ms2_tolerance_in_ppm, max_peak_num, normalize_intensity);
// Calculate spectral entropy
float spectral_entropy = calculate_spectral_entropy(*spec_a, spec_a_len);
printf("Spectral Entropy: %f\n", spectral_entropy);
}
// Calculate entropy similarity
{
int spec_a_len = 3;
float spec_a[3][2] = {{169.071, 7.917962}, {186.066, 1.021589}, {186.0769, 100.0}};
int spec_b_len = 3;
float spec_b[3][2] = {{120.212, 37.16}, {169.071, 66.83}, {186.066, 999.0}};
// The parameters for calculate_entropy_similarity function.
int clean_spectra = 1;
float ms2_tolerance_in_da = 0.02, ms2_tolerance_in_ppm = -1;
float min_mz= 0, max_mz = -1;
float noise_threshold = 0.01;
int max_peak_num = -1;
// Calculate entropy similarity, the data in spec_a and spec_b will modified.
float similarity = calculate_entropy_similarity(*spec_a, spec_a_len, *spec_b, spec_b_len, ms2_tolerance_in_da, ms2_tolerance_in_ppm, clean_spectra, min_mz, max_mz, noise_threshold, max_peak_num);
printf("Entropy Similarity: %f\n", similarity);
}
An example is available in folder languages/c folder and Example.c, CMakeLists.txt
For JavaScript users
An example is available in folder languages/javascript folder and example.js
You can also refer to the MSViewer repository for an example of how to integrate this package into a web application.
Metadata
Release files for ms-entropy 1.5.3
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