<jupyter_start><jupyter_text>Title: "Figure_6_10Abs"Code Author: "<NAME>, Ph.D."<jupyter_code>import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns<jupyter_output><empty_output><jupyter_text>Neural Network performance on antibody-escape phenotype for 10 human monoclonal antibodies (Figure 6B; Table S3)<jupyter_code>d_Abs = {'COV2-2050': [0.246994], 'COV2-2082': [0.09224], 'COV2-2094': [0.315764], 'COV2-2096': [0.19175], 'COV2-2165': [0.123021], 'COV2-2479': [0.130445], 'COV2-2499': [0.260286], 'COV2-2677': [0.29153], 'COV2-2832': [0.125652], 'rCR3022': [0.264203]}
df_perf = pd.DataFrame(data=d_Abs, index=["Performance"])
df_perf.iloc[0,]
plt.figure(figsize=(8,6))
sns.scatterplot(df_perf.columns, df_perf.iloc[0,],hue=df_perf.columns,legend = False)
plt.ylim(0,0.4)
plt.xticks(rotation=45)
plt.title("Neural network model performance", fontsize=16)
plt.ylabel("Spearman correlation coefficient",fontsize=16)
plt.show()<jupyter_output>/Users/bowang/miniforge3/envs/fcyto/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(<jupyter_text>Neural network derived antibody-escape phenotype for 10 human monoclonal antibodies (Figure 6A)<jupyter_code>df_pred= pd.read_csv("../Results/NN_Derived_Phenotypic_Scores/10Abs_pred_score.csv")
plt.figure(figsize=(20,5))
g=sns.scatterplot(x="site",y="pred_inv_boxcox",data=df_pred,hue="Ab type")
plt.xticks(np.arange(330, max(df_pred["site"])+1, 5))
plt.xlabel("Residue index",fontsize=16)
plt.ylabel("Antibody escape fraction",fontsize=16)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15199562602515035, 'fraction_numerical': 0.08802624384909787, 'mean_word_length': 7.840579710144928, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '22847', 'license': 'MIT', 'n_tokens_mistral': 806, 'n_tokens_neox': 695, 'n_words': 135, 'path': 'Code/Figure_6_10Abs.ipynb', 'repo_name': 'gamazonlab/DeepMutScan', 'size': '1830'}