<jupyter_start><jupyter_text>Sequence & Repertoire Heatmaps Following training the VAE, one mode of visualizing the data is through clustermaps.   Sequence Heatmap We can view sequences by their labels and featurs through the following command:<jupyter_code>%%capture
import sys
sys.path.append('../../')
from DeepTCR.DeepTCR import DeepTCR_U

# Instantiate training object
DTCRU = DeepTCR_U('Tutorial')

#Load Data from directories
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)

#Train VAE
DTCRU.Train_VAE(Load_Prev_Data=False)
DTCRU.HeatMap_Sequences()<jupyter_output><empty_output><jupyter_text>Sample/Repertoire Heatmap By taking the average feature value across a sample, we can also construct a heatmap where we have samples by average feature values color coded by the labels.<jupyter_code>DTCRU.HeatMap_Samples()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.08460754332313965, 'fraction_numerical': 0.004077471967380225, 'mean_word_length': 6.671875, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '296297', 'license': 'MIT', 'n_tokens_mistral': 330, 'n_tokens_neox': 316, 'n_words': 82, 'path': 'tutorials/unsupervised/3 - Sequence & Repertoire Level Heatmaps.ipynb', 'repo_name': 'AlyssaBlack/DeepTCR', 'size': '981'}