import os from scipy.stats import ttest_ind #Run VAE DTCRU = DeepTCR_U('Sequence_C', device=1) DTCRU.Get_Data(directory='../../Data/Murine_Antigens', Load_Prev_Data=False, aggregate_by_aa=True, aa_column_beta=0, count_column=1, v_beta_column=2, j_beta_column=3) graph_seed = 0 split_seed = 0 DTCRU.Train_VAE(Load_Prev_Data=False, graph_seed=graph_seed, split_seed=split_seed) distances_vae_seq_gene = pdist(DTCRU.features, metric='euclidean') distances_list = [distances_vae_seq_gene] names = ['VAE-Seq-VDJ'] dir_results = 'sup_v_unsup_results' if not os.path.exists(dir_results): os.makedirs(dir_results) df_metrics = Assess_Performance_KNN(distances_list, names, DTCRU.class_id, dir_results, metrics=['AUC'])
os.makedirs(dir_results) # Instantiate training object DTCRU = DeepTCR_U('Repertoire_Classification') DTCRU.Get_Data(directory='../../Data/Rudqvist', Load_Prev_Data=False, aggregate_by_aa=True, aa_column_beta=1, count_column=2, v_beta_column=7, d_beta_column=14, j_beta_column=21) # VAE-Gene DTCRU.Train_VAE(use_only_gene=True) d_vae_gene = squareform(pdist(DTCRU.features)) prop_vae_gene, _ = phenograph_clustering_freq(d_vae_gene, DTCRU) # VAE-Seq DTCRU.Train_VAE(use_only_seq=True) d_vae_seq = squareform(pdist(DTCRU.features)) prop_vae_seq, _ = phenograph_clustering_freq(d_vae_seq, DTCRU) # VAE-Seq-Gene DTCRU.Train_VAE(Load_Prev_Data=False) d_vae_seq_gene = squareform(pdist(DTCRU.features)) prop_vae_seq_gene, _ = phenograph_clustering_freq(d_vae_seq_gene, DTCRU) # Hamming d_hamming = squareform(pdist(np.squeeze(DTCRU.X_Seq_beta, 1),
# Instantiate training object DTCRU = DeepTCR_U('Rep_Dendrogram', device='/device:GPU:1') #Load Data from directories DTCRU.Get_Data(directory='../../Data/Rudqvist', Load_Prev_Data=True, aggregate_by_aa=True, aa_column_beta=1, count_column=2, v_beta_column=7, d_beta_column=14, j_beta_column=21) #Train VAE DTCRU.Train_VAE(accuracy_min=0.9, Load_Prev_Data=True) #Create Repertoire Dendrogram color_dict = { 'Control': 'limegreen', '9H10': 'red', 'RT': 'darkorange', 'Combo': 'magenta' } DTCRU.Repertoire_Dendrogram(n_jobs=40, distance_metric='KL', log_scale=True, dendrogram_radius=0.28, repertoire_radius=0.35, Load_Prev_Data=True, gridsize=60,
from DeepTCR.DeepTCR import DeepTCR_U # Instantiate training object DTCRU = DeepTCR_U('Rep_Dendrogram',device='/gpu:2') #Load Data from directories DTCRU.Get_Data(directory='../../Data/Rudqvist',Load_Prev_Data=False,aggregate_by_aa=True, aa_column_beta=1,count_column=2,v_beta_column=7,d_beta_column=14,j_beta_column=21) #Train VAE DTCRU.Train_VAE(accuracy_min=0.9) color_dict = {'Control':'limegreen','9H10':'red','RT':'darkorange','Combo':'magenta'} DTCRU.Repertoire_Dendrogram(n_jobs=40,distance_metric='KL', dendrogram_radius=0.28,repertoire_radius=0.35,Load_Prev_Data=True,gridsize=6, color_dict=color_dict)
v_beta = DTCR.v_beta j_beta = DTCR.j_beta d_beta = DTCR.d_beta hla = DTCR.hla_data_seq sample_id = DTCR.sample_id file = 'cm038_x2_u.pkl' featurize = False if featurize: DTCR_U = DeepTCR_U('test_hum', device='/device:GPU:6') DTCR_U.Load_Data(beta_sequences=beta_sequences, v_beta=v_beta, d_beta=d_beta, j_beta=j_beta, hla=hla) DTCR_U.Train_VAE(Load_Prev_Data=False, latent_dim=64, stop_criterion=0.01) X_2 = umap.UMAP().fit_transform(DTCR_U.features) with open(file, 'wb') as f: pickle.dump(X_2, f, protocol=4) else: with open(file, 'rb') as f: X_2 = pickle.load(f) df_plot['x'] = X_2[:, 0] df_plot['y'] = X_2[:, 1] def histogram_2d_cohort(d, w, grid_size): # center of data d_center = np.mean(np.concatenate(d, axis=0), axis=0) # largest radius
# Assess ability for structural entropy to be of measure of number of antigens classes_all = np.array([ 'Db-F2', 'Kb-M38', 'Db-M45', 'Db-NP', 'Db-PA', 'Db-PB1', 'Kb-m139', 'Kb-SIY', 'Kb-TRP2' ]) DTCRU.Get_Data(directory='../../Data/Murine_Antigens', Load_Prev_Data=False, aggregate_by_aa=True, aa_column_beta=0, count_column=1, v_beta_column=2, j_beta_column=3) # VAE-Gene DTCRU.Train_VAE(use_only_gene=True) d_vae_gene = squareform(pdist(DTCRU.features)) # VAE-Seq DTCRU.Train_VAE(use_only_seq=True) d_vae_seq = squareform(pdist(DTCRU.features)) # VAE-Seq-Gene DTCRU.Train_VAE() d_vae_seq_gene = squareform(pdist(DTCRU.features)) # Hamming d_hamming = squareform(pdist(np.squeeze(DTCRU.X_Seq_beta, 1), metric='hamming')) # Kmer