| |
| """ |
| 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")] |
|
|
| |
| 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: |
| |
| 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/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.show() |
|
|
|
|
| |
| 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.show() |
|
|
|
|
|
|
