ms-entropy 1.4.2

Python implementation of spectral entropy, entropy similarity, and Flash entropy search for mass spectrometry data.

If you have any questions, feel free to contact me via email: [e-mail]. 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.