File size: 7,281 Bytes
fb5f46a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | # -*- coding: utf-8 -*-
"""
Created on Thu Sep 7 08:49:29 2023
@author: DELL
"""
import umap
import hnswlib
import pickle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matchms.filtering as msfilters
from sklearn.preprocessing import scale
from sklearn.decomposition import PCA
from matchms.importing import load_from_mgf
from gensim.models import Word2Vec
from rdkit import Chem
from rdkit.Chem import DataStructs, AllChem, Draw
from spec2vec import SpectrumDocument
from spec2vec.vector_operations import calc_vector
from core.identification import identify_unknown, match_spectrum
def plot_spectrum_comparison(s1, s2, mzrange, loss=False):
if not loss:
plt.vlines(s1.mz, ymin=0, ymax=s1.intensities / np.max(s1.intensities), color='r', lw = 1, label = 'query')
plt.vlines(s2.mz, ymin=0, ymax=-s2.intensities / np.max(s2.intensities), color='b', lw = 1, label = 'neigbor')
else:
s1 = msfilters.add_losses(s1)
s2 = msfilters.add_losses(s2)
plt.vlines(s1.losses.mz, ymin=0, ymax=s1.losses.intensities / np.max(s1.losses.intensities), color='r', lw = 1, label = 'query')
plt.vlines(s2.losses.mz, ymin=0, ymax=-s2.losses.intensities / np.max(s2.losses.intensities), color='b', lw = 1, label = 'neigbor')
plt.axhline(y=0,color='black', lw = 1)
plt.ylabel('abundance')
plt.xlim(mzrange)
database = pd.read_csv('data/DeepMassStructureDB-v1.0.csv')
spectrums = [s for s in load_from_mgf("D:/DeepMASS2_Data_Processing/Example/CASMI/all_casmi.mgf")]
# Example 1
s = spectrums[255]
print(s.metadata)
model = Word2Vec.load("model/Ms2Vec_allGNPSnegative.hdf5")
p = hnswlib.Index(space='l2', dim=300)
p.load_index('data/references_index_negative_spec2vec.bin')
with open('data/references_spectrums_negative.pickle', 'rb') as file:
references = pickle.load(file)
references = np.array(references)
precursors = [s.get('precursor_mz') for s in references]
precursors = np.array(precursors)
s_metadata = s.metadata
s_matchms = match_spectrum(s, precursors, references)
s_matchms_metadata = s_matchms.metadata
s_deepmass = identify_unknown(s, p, model, references, database)
s_deepmass_metadata = s_deepmass.metadata
get_mol_fingerprint = lambda x: AllChem.GetMorganFingerprintAsBitVect(Chem.MolFromSmiles(x), radius=2)
get_mol_similarity = lambda x, y: DataStructs.FingerprintSimilarity(x, y)
calc_ms2vec_vector = lambda x: calc_vector(model, SpectrumDocument(x, n_decimals=2))
deepmass_candidate = s_deepmass_metadata['annotation']['CanonicalSMILES']
deepmass_reference = s_deepmass_metadata['reference']
deepmass_candidate_vector = []
for r in deepmass_candidate:
# a = calc_ms2vec_vector(s)
b = get_mol_fingerprint(r)
deepmass_candidate_vector.append(list(b))
deepmass_candidate_vector = np.array(deepmass_candidate_vector)
deepmass_reference_vector = []
for r in deepmass_reference:
# a = calc_ms2vec_vector(r)
b = get_mol_fingerprint(r.get('smiles'))
deepmass_reference_vector.append(list(b))
deepmass_reference_vector = np.array(deepmass_reference_vector)
X = np.vstack((deepmass_candidate_vector, deepmass_reference_vector))
pca = umap.UMAP(n_components = 2)
X_r = pca.fit_transform(scale(X))
Draw.MolToFile(Chem.MolFromSmiles(s.get('smiles')), 'temp/temp1.png')
a, b = len(deepmass_candidate_vector), len(deepmass_reference_vector)
plt.figure(dpi = 300, figsize = (3.5, 3.5))
plt.scatter(X_r[1:a,0], X_r[1:a,1], color = 'green', alpha = 0.5, label = 'Candidates')
plt.scatter(X_r[a:a+10,0], X_r[a:a+10,1], color = 'blue', alpha = 0.5, label = 'Top 10 Neigbors')
plt.scatter(X_r[0,0], X_r[0,1], color = 'red', alpha = 0.8, label = 'True Annotation')
plt.scatter(X_r[55,0], X_r[55,1], color = 'orange', alpha = 0.5)
plt.xlabel('Dim 1')
plt.ylabel('Dim 2')
plt.xlim(24, 41)
plt.ylim(-16, -9)
plt.legend(loc = 'upper right')
plt.show()
plt.figure(dpi = 300, figsize = (3, 4.8))
plt.subplot(311)
plot_spectrum_comparison(s, deepmass_reference[9], (50,250))
plt.subplot(312)
plot_spectrum_comparison(s, deepmass_reference[0], (50,250))
plt.subplot(313)
plot_spectrum_comparison(s, deepmass_reference[3], (50,250))
plt.subplots_adjust(hspace=0.5)
plt.xlabel('m/z')
# plt.legend(loc='upper center', bbox_to_anchor=(1.2, 4))
plt.show()
# Example 2
s = spectrums[368]
print(s.metadata)
model = Word2Vec.load("model/Ms2Vec_allGNPSpositive.hdf5")
p = hnswlib.Index(space='l2', dim=300)
p.load_index('data/references_index_positive_spec2vec.bin')
with open('data/references_spectrums_positive.pickle', 'rb') as file:
references = pickle.load(file)
references = np.array(references)
precursors = [s.get('precursor_mz') for s in references]
precursors = np.array(precursors)
model = Word2Vec.load("model/Ms2Vec_allGNPSpositive.hdf5")
p = hnswlib.Index(space='l2', dim=300)
p.load_index('data/references_index_positive_spec2vec.bin')
with open('data/references_spectrums_positive.pickle', 'rb') as file:
references = pickle.load(file)
references = np.array(references)
precursors = [s.get('precursor_mz') for s in references]
precursors = np.array(precursors)
s_metadata = s.metadata
s_matchms = match_spectrum(s, precursors, references)
s_matchms_metadata = s_matchms.metadata
s_deepmass = identify_unknown(s, p, model, references, database)
s_deepmass_metadata = s_deepmass.metadata
get_mol_fingerprint = lambda x: AllChem.GetMorganFingerprintAsBitVect(Chem.MolFromSmiles(x), radius=2)
get_mol_similarity = lambda x, y: DataStructs.FingerprintSimilarity(x, y)
calc_ms2vec_vector = lambda x: calc_vector(model, SpectrumDocument(x, n_decimals=2))
deepmass_candidate = s_deepmass_metadata['annotation']['CanonicalSMILES']
deepmass_reference = s_deepmass_metadata['reference']
deepmass_candidate_vector = []
for r in deepmass_candidate:
b = get_mol_fingerprint(r)
deepmass_candidate_vector.append(list(b))
deepmass_candidate_vector = np.array(deepmass_candidate_vector)
deepmass_reference_vector = []
for r in deepmass_reference:
b = get_mol_fingerprint(r.get('smiles'))
deepmass_reference_vector.append(list(b))
deepmass_reference_vector = np.array(deepmass_reference_vector)
X = np.vstack((deepmass_candidate_vector, deepmass_reference_vector))
pca = umap.UMAP(n_components = 2)
X_r = pca.fit_transform(scale(X))
Draw.MolToFile(Chem.MolFromSmiles(s.get('smiles')), 'temp/temp.png')
a, b = len(deepmass_candidate_vector), len(deepmass_reference_vector)
plt.figure(dpi = 300, figsize = (3.5, 3.5))
plt.scatter(X_r[1:a,0], X_r[1:a,1], color = 'green', alpha = 0.5, label = 'Candidates')
plt.scatter(X_r[0,0], X_r[0,1], color = 'red', alpha = 0.8, label = 'True Annotation')
plt.scatter(X_r[a:a+10,0], X_r[a:a+10,1], color = 'blue', alpha = 0.5, label = 'Top 10 Neigbors')
plt.scatter(X_r[a+9:a+10,0], X_r[a+9:a+10,1], color = 'orange', alpha = 0.5)
plt.xlabel('Dim 1')
plt.ylabel('Dim 2')
plt.legend()
plt.figure(dpi = 300, figsize = (3, 4.8))
plt.subplot(311)
plot_spectrum_comparison(s, deepmass_reference[0], (0,150), loss=True)
plt.subplot(312)
plot_spectrum_comparison(s, deepmass_reference[1], (100,350))
plt.subplot(313)
plot_spectrum_comparison(s, deepmass_reference[9], (100,350))
plt.subplots_adjust(hspace=0.5)
plt.xlabel('m/z')
# plt.legend(loc='upper center', bbox_to_anchor=(1.2, 4))
plt.show()
|