To search spectral files with entropy similarity, you can download pre-compiled program from here. For advanced user who want to calculate spectral entropy / entropy similarity / other spectral similarity by themself, please use the Python code below.
Python 3.7, numpy>=1.17.4, scipy>=1.3.2
cython>=0.29.13 (Not required but highly recommended)
# The command below is not required but strongly recommended, as it will compile the cython code to run faster
python setup.py build_ext --inplaceTo calculate spectral entropy, the spectrum need to be centroid first. When you are focusing on fragment ion's information, the precursor ion may need to be removed from the spectrum before calculating spectral entropy.
Calculate spectral entropy for centroid spectrum with python is very simple (just one line with scipy package).
import numpy as np
import scipy.stats
spectrum = np.array([[41.04, 37.16], [69.07, 66.83], [86.1, 999.0]], dtype=np.float32)
entropy = scipy.stats.entropy(spectrum[:, 1])
print("Spectral entropy is {}.".format(entropy))
# The output should be: Spectral entropy is 0.3737888038158417.
print('-' * 30)For profile spectrum which haven't been centroid, you can use a clean_spectrum to centroid the spectrum, for
example:
import numpy as np
import scipy.stats
import spectral_entropy
spectrum = np.array([[69.071, 7.917962], [86.066, 1.021589], [86.0969, 100.0]], dtype=np.float32)
spectrum = spectral_entropy.clean_spectrum(spectrum)
entropy = scipy.stats.entropy(spectrum[:, 1])
print("Spectral entropy is {}.".format(entropy))
# The output should be: Entropy similarity:0.2605222463607788.
print('-' * 30)We provide a function clean_spectrum to help you remove precursor ion, centroid spectrum and remove noise ions.
For example:
import numpy as np
import spectral_entropy
spectrum = np.array([[41.04, 0.3716], [69.071, 7.917962], [69.071, 100.], [86.0969, 66.83]], dtype=np.float32)
clean_spectrum = spectral_entropy.clean_spectrum(spectrum,
max_mz=85,
noise_removal=0.01,
ms2_da=0.05)
print("Clean spectrum will be:{}".format(clean_spectrum))
# The output should be: Clean spectrum will be:[[69.071 1. ]]
print('-' * 30)Before calculate entropy similarity, the spectrum need to be centroid first. Remove the noise ions is highly recommend. Also, base on our test on NIST20 and Massbank.us database, remove ions have m/z higher than precursor ion's m/z - 1.6 will greatly improve the spectral identification performance.
We provide calculate_entropy_similarity function to calculate two spectral entropy.
import numpy as np
import spectral_entropy
spec_query = np.array([[69.071, 7.917962], [86.066, 1.021589], [86.0969, 100.0]], dtype=np.float32)
spec_reference = np.array([[41.04, 37.16], [69.07, 66.83], [86.1, 999.0]], dtype=np.float32)
# Calculate entropy similarity.
similarity = spectral_entropy.calculate_entropy_similarity(spec_query, spec_reference, ms2_da=0.05)
print("Entropy similarity:{}.".format(similarity))
# The output should be: Entropy similarity:0.8984397722577456.
print('-' * 30)We also provide 44 different spectral similarity algorithm for MS/MS spectral comparison
You can find the detail reference here: https://SpectralEntropy.readthedocs.io/en/master/
Before calculating spectral similarity, it's highly recommended to remove spectral noise. For example, peaks have intensity less than 1% maximum intensity can be removed to improve identificaiton performance.
import numpy as np
import spectral_entropy
spec_query = np.array([[69.071, 7.917962], [86.066, 1.021589], [86.0969, 100.0]], dtype=np.float32)
spec_reference = np.array([[41.04, 37.16], [69.07, 66.83], [86.1, 999.0]], dtype=np.float32)
# Calculate entropy similarity.
similarity = spectral_entropy.similarity(spec_query, spec_reference, method="entropy",
ms2_da=0.05)
print("Entropy similarity:{}.".format(similarity))
# The output should be: Entropy similarity:0.8984397722577456.
print('-' * 30)
# Calculate unweighted entropy similarity.
similarity = spectral_entropy.similarity(spec_query, spec_reference, method="unweighted_entropy",
ms2_da=0.05)
print("Unweighted entropy similarity:{}.".format(similarity))
# The output should be: Unweighted entropy similarity:0.9826668790176113.
print('-' * 30)
# Calculate all similarity.
all_dist = spectral_entropy.all_similarity(spec_query, spec_reference, ms2_da=0.05)
for dist_name in all_dist:
method_name = spectral_entropy.methods_name[dist_name]
print("Method name: {}, similarity score:{}.".format(method_name, all_dist[dist_name]))
# A list of different spectral similarity will be shown."entropy": Entropy distance
"unweighted_entropy": Unweighted entropy distance
"euclidean": Euclidean distance
"manhattan": Manhattan distance
"chebyshev": Chebyshev distance
"squared_euclidean": Squared Euclidean distance
"fidelity": Fidelity distance
"matusita": Matusita distance
"squared_chord": Squared-chord distance
"bhattacharya_1": Bhattacharya 1 distance
"bhattacharya_2": Bhattacharya 2 distance
"harmonic_mean": Harmonic mean distance
"probabilistic_symmetric_chi_squared": Probabilistic symmetric χ2 distance
"ruzicka": Ruzicka distance
"roberts": Roberts distance
"intersection": Intersection distance
"motyka": Motyka distance
"canberra": Canberra distance
"baroni_urbani_buser": Baroni-Urbani-Buser distance
"penrose_size": Penrose size distance
"mean_character": Mean character distance
"lorentzian": Lorentzian distance
"penrose_shape": Penrose shape distance
"clark": Clark distance
"hellinger": Hellinger distance
"whittaker_index_of_association": Whittaker index of association distance
"symmetric_chi_squared": Symmetric χ2 distance
"pearson_correlation": Pearson/Spearman Correlation Coefficient
"improved_similarity": Improved Similarity
"absolute_value": Absolute Value Distance
"dot_product": Dot-Product (cosine)
"dot_product_reverse": Reverse dot-Product (cosine)
"spectral_contrast_angle": Spectral Contrast Angle
"wave_hedges": Wave Hedges distance
"cosine": Cosine distance
"jaccard": Jaccard distance
"dice": Dice distance
"inner_product": Inner Product distance
"divergence": Divergence distance
"avg_l": Avg (L1, L∞) distance
"vicis_symmetric_chi_squared_3": Vicis-Symmetric χ2 3 distance
"ms_for_id_v1": MSforID distance version 1
"ms_for_id": MSforID distance
"weighted_dot_product": Weighted dot product distance"