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import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
import matplotlib.colors as mcolors
import matplotlib.patches as mpatches
from matplotlib import font_manager
import matplotlib.patches as patches
from sklearn.metrics import roc_curve, auc, r2_score
from fuson_plm.utils.visualizing import set_font
global caid2_winners, caid2_model_rankings
caid2_winners = pd.DataFrame(data=
{
'Model Name': ['Dispredict3','flDPnn2','flDPnn','flDPlr','flDPlr2','DisoPred',
'IDP-Fusion','ESpritz-D','DeepIDP-2L','disomine','DISOPRED3-diso','IUPred3',
'AlphaFold-rsa','AlphaFold-pLDDT'], # do the top 6 models, and IUPred because it's well-known
'AUROC': [0.838,0.836,0.833,0.827,0.821,0.821,
0.818,0.802,0.800,0.797,0.692,0.755,0.747,0.695],
})
caid2_winners['Model Type'] = ['caid2_competition']*len(caid2_winners)
caid2_winners['Model Epoch'] = [np.nan]*len(caid2_winners)
caid2_model_rankings = {
'Dispredict3': 1,
'flDPnn2': 2,
'flDPnn': 3,
'flDPlr': 4,
'flDPlr2': 5,
'DisoPred': 6,
'IDP-Fusion': 7,
'ESpritz-D': 8,
'DeepIDP-2L': 9,
'disomine': 10,
'DISOPRED3-diso': 35,
'IUPred3': 21,
'AlphaFold-rsa': 24,
'AlphaFold-pLDDT': 34
}
# Method for lengthening the model name
def lengthen_model_name(row):
model_type = row['Model Type']
name = row['Model Name']
epoch = row['Model Epoch']
if 'esm' in name:
return name
if 'puncta' in name:
return name
if model_type=='caid2_competition':
return name
return f'{name}_e{epoch}'
# Method for shortening the model name for display
def shorten_model_name(row):
model_type = row['Model Type']
name = row['Model Name']
epoch = row['Model Epoch']
if 'esm' in name:
return 'ESM-2-650M'
if model_type=='caid2_competition':
return name
if 'snp_' in name:
prob_type = 'snp'
elif 'uniform_' in name:
prob_type = 'uni'
layers = name.split('layers')[0].split('_')[-1]
maskrate = name.split('mask')[1].split('-', 1)[0]
kqv_tag = name.split('layers_')[1].split('_')[0]
dt = name.split('mask')[1].split('-', 1)[1]
return f'{prob_type}_{layers}L_{kqv_tag}_mask{maskrate}_{dt}_e{epoch}'
def make_heatmap(df, results_dir='.', gold_standard_model_name="esm2_t33_650M_UR50D",split="test",thresh=None,ax=None):
# Set font to Ubuntu
set_font()
# Declare columns to compare: metrics
columns_to_compare = ['AUROC']
# Define the literature-reported values for CAID competition winners - only IF the split is not "benchmark"
if not(split=="benchmark"):
df = pd.concat([df,caid2_winners])
# Create Short Model Name and Full Model Name columns for later use
df['Model Epoch'] = df['Model Epoch'].apply(lambda x: str(int(x)) if not(np.isnan(x)) else '')
df['Short Model Name'] = df.apply(lambda row: shorten_model_name(row),axis=1)
df['Full Model Name'] = df.apply(lambda row: lengthen_model_name(row), axis=1)
# Isolate gold standard row for later comparison
gold_standard = df[df['Full Model Name'] == gold_standard_model_name].reset_index(drop=True).iloc[0]
gold_standard_short_model_name = df[df['Full Model Name'] == gold_standard_model_name]['Short Model Name'].item()
# Create a new dataframe for the heatmap; sort by model type and place gold standard on top
heatmap_data = df[['Model Type','Short Model Name','Full Model Name'] + columns_to_compare].copy()
heatmap_data['is_gold_standard'] = (heatmap_data['Full Model Name'] == gold_standard_model_name).astype(int)
heatmap_data = heatmap_data.sort_values(by=['is_gold_standard','Model Type','AUROC'], ascending=[False,True,False]).reset_index(drop=True).drop(columns=['is_gold_standard'])
# Save the original values before calculating differences so we can use them for annotation
original_values = heatmap_data[columns_to_compare].copy()
# Calculate differences from the gold standard
for col in columns_to_compare:
heatmap_data[col] = heatmap_data[col] - gold_standard[col]
# Create a color map where values equal to 0 are white, above are red, and below are blue
cmap = sns.color_palette("coolwarm", as_cmap=True) # other option is diverging_palette(220, 20, as_cmap=True)
### Make the plot
# can plot on a bigger plot, or make it an individual plot
if ax is None:
tallsize = max(8, 8 +.25*(len(heatmap_data)-26))
fig, ax = plt.subplots(1, 1, figsize=(8, tallsize), dpi=300)
# Plot the heatmap with original values as annotations
hm = sns.heatmap(heatmap_data.set_index('Short Model Name').drop(columns=['Model Type','Full Model Name']),
annot=False, fmt='', cmap=cmap, center=0,
cbar_kws={'label': 'Difference from Gold Standard'})
# Explicitly set tick labels to prevent them from being messed up
ax.set_yticklabels(heatmap_data['Short Model Name'], rotation=0, fontsize=12)
# Add padding to the y-axis label
ax.set_ylabel("Short Model Name", labelpad=20) # Increase the labelpad value to add more padding
# Bold any values values that exceed the gold standard
for i in range(original_values.shape[0]):
for j in range(original_values.shape[1]):
value = original_values.iloc[i, j]
if value > gold_standard[columns_to_compare[j]]:
ax.text(j + 0.5, i + 0.5, f'{value:.3f}', ha='center', va='center', fontweight='bold', color='black')
else:
ax.text(j + 0.5, i + 0.5, f'{value:.3f}', ha='center', va='center', color='black')
# Add horizontal lines between different model types
model_type_series = heatmap_data['Model Type'].values
last_index = 0
labels_positions = [] # To store the positions for labels
for i in range(1, len(model_type_series)):
if model_type_series[i] != model_type_series[i - 1]:
hm.axhline(i, color='white', linewidth=8) # Draw a thick white line between groups
labels_positions.append((last_index + i) / 2) # Store the midpoint for labeling
last_index = i
# Add label for the last group
labels_positions.append((last_index + len(model_type_series)) / 2)
# Italic and bold models that win AUROC; apply yellow coloring to gold standard model
for ytick, model_name in enumerate(heatmap_data['Short Model Name']):
if model_name == gold_standard_short_model_name:
# color yellow
label = ax.get_yticklabels()[ytick]
#label.set_color('gold')
label.set_bbox(dict(facecolor='gold', alpha=0.5, edgecolor='gold'))
if model_name != gold_standard_short_model_name:
auroc_value = original_values.loc[ytick, 'AUROC']
# Apply bold and italic for wins on either AUROC or F1 Score
if (auroc_value > gold_standard['AUROC']):
label = ax.get_yticklabels()[ytick]
#label.set_style('italic')
#label.set_weight('bold')
label.set_bbox(dict(facecolor='red', alpha=0.3, edgecolor='red'))
# Make legend
gold_patch = mpatches.Patch(color='gold', alpha=0.5, label='Gold Standard')
red_patch = mpatches.Patch(color='red', alpha=0.5, label='Winner')
plt.legend(handles=[gold_patch, red_patch], loc='best', bbox_to_anchor=(0, 0)) # You can change loc to position the legend
split_fname_dict = {
"testing": "CAID2_test",
"training": "CAID2_train",
"benchmark": "FusionPDB_pLDDT_disorder"
}
split_title_dict = {
"testing": "CAID-2 Disorder Prediction",
"training": "CAID-2 Disorder Prediction",
"benchmark": "FusionPDB_pLDDT Disorder Prediction"
}
ax.set_title(split_title_dict[split])
# Rotate the color bar label
cbar = hm.collections[0].colorbar
cbar.ax.yaxis.set_label_position('right')
cbar.ax.yaxis.set_ticks_position('right')
cbar.set_label('Difference from Gold Standard', rotation=270, labelpad=20) # Rotate 270 degrees and add some padding
# Set tight layout using fig
fig.tight_layout(rect=[0, 0, 0.95, 1]) # Add extra padding on the right side to fit the label
plt.savefig(f"{results_dir}/{split_fname_dict[split]}_heatmap_vs_{gold_standard_model_name}.png")
# Plot AUROC curve of ONE model of interest on its fusion pdb performance
def make_benchmark_auroc_curve(results_dir='.', seq_label_dict=None, path_to_results_of_interest='', model_alias=None):
# Isolate the information for the model we'll be plotting
benchmark_model = path_to_results_of_interest.split('trained_models/')[1].split('/')
benchmark_model_type = benchmark_model[0]
benchmark_model_epoch = np.nan
benchmark_model_hyperparams = None
if len(benchmark_model)==5:
benchmark_model_name = benchmark_model[1]
benchmark_model_epoch = benchmark_model[2].split('epoch')[1]
benchmark_model_hyperparams = benchmark_model[3]
else:
benchmark_model_name = benchmark_model[0]
benchmark_model_hyperparams = benchmark_model[1]
benchmark_model_info = pd.DataFrame(data={
'Model Type': [benchmark_model_type], 'Model Name': [benchmark_model_name], 'Model Epoch': [benchmark_model_epoch]
})
if model_alias is None:
model_alias = benchmark_model_info.apply(lambda row: shorten_model_name(row),axis=1).iloc[0]
color_map = {
model_alias: 'black'
}
method_results = {model_alias: path_to_results_of_interest}
method_results = {k:v for k,v in method_results.items() if v not in [None, '']}
set_font()
plt.figure(figsize=(10,6),dpi=300)
# To store AUROC values and corresponding labels for sorting
roc_data = []
# Read each result file and plot the metrics
for method, path in method_results.items():
df = pd.read_csv(path) # columns = prob_1,labels
# Extract probabilities and labels
prob_1 = ",".join(df['prob_1'].tolist())
df['labels'] = df['sequence'].apply(lambda x: seq_label_dict[x])
labels = "".join(df['labels'].tolist())
prob_1 = [float(x) for x in prob_1.split(",")]
labels = [int(x) for x in list(labels)]
sequences = "".join(df['sequence'].tolist())
assert len(prob_1)==len(labels)==len(sequences)
# Compute ROC curve and ROC area
fpr, tpr, thresholds = roc_curve(labels, prob_1)
roc_auc = auc(fpr, tpr)
# Store data for sorting later
roc_data.append((method, fpr, tpr, roc_auc))
# Sort the methods by AUROC values
roc_data = sorted(roc_data, key=lambda x: x[3], reverse=True)
# Plot sorted ROC curves
for method, fpr, tpr, roc_auc in roc_data:
if method == model_alias:
plt.plot(fpr, tpr, color=color_map[method], lw=2, label=f'{method} ({roc_auc:0.3f})')
else:
plt.plot(fpr, tpr, color=color_map[method], lw=1, alpha=0.7, label=f'{method} ({roc_auc:0.3f})')
# Set other stylistic elements
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.plot([0, 1], [0, 1], color='darkgrey', lw=2, linestyle='--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve')
# After plotting the ROC curves, customize the legend
handles, labels = plt.gca().get_legend_handles_labels()
# Create the legend first
legend = plt.legend(handles, labels, loc="center left", bbox_to_anchor=(1, 0.5))
# Iterate through the legend's text labels
for text in legend.get_texts():
if model_alias in text.get_text():
text.set_fontweight('bold') # Bold the alias model
plt.tight_layout()
plt.savefig(f'{results_dir}/FusionPDB_pLDDT_disorder_{model_alias}_AUROC_curve.png')
# Plot AUROC curve of ONE model of interest with all the CAID models
def make_auroc_curve(results_dir='.', seq_label_dict=None, seq_ids_dict=None, path_to_results_of_interest='', model_alias=None, path_to_esm_results=None, with_rankings=False):
# Isolate the information for the model we'll be plotting
benchmark_model = path_to_results_of_interest.split('trained_models/')[1].split('/')
benchmark_model_type = benchmark_model[0]
benchmark_model_epoch = np.nan
benchmark_model_hyperparams = None
if len(benchmark_model)==5:
benchmark_model_name = benchmark_model[1]
benchmark_model_epoch = benchmark_model[2].split('epoch')[1]
benchmark_model_hyperparams = benchmark_model[3]
else:
benchmark_model_name = benchmark_model[0]
benchmark_model_hyperparams = benchmark_model[1]
benchmark_model_info = pd.DataFrame(data={
'Model Type': [benchmark_model_type], 'Model Name': [benchmark_model_name], 'Model Epoch': [benchmark_model_epoch]
})
if model_alias is None:
model_alias = benchmark_model_info.apply(lambda row: shorten_model_name(row),axis=1).iloc[0]
color_map = {
'Dispredict3': '#d62727', #1
'flDPnn2': '#ff7f0f', #2
'flDPnn': '#1f77b4', #3
'flDPlr': '#bcbd21', #4
'flDPlr2': '#16becf', #5
'DisoPred': '#1f77b4', #6
'IDP-Fusion': '#d62727', #7
'ESpritz-D': '#8b564c', #8
'DeepIDP-2L': '#e377c2', #9
'disomine': '#e377c2', #10
'DISOPRED3-diso': '#ff892d',
'IUPred3': '#8b564c',
'AlphaFold-rsa': '#2ba02b',
'AlphaFold-pLDDT': '#ff892d',
model_alias: 'black'
}
method_results = {'Dispredict3': 'processed_data/caid2_competition_results/Dispredict3_CAID-2_Disorder_NOX.csv',
'flDPnn2': 'processed_data/caid2_competition_results/flDPnn2_CAID-2_Disorder_NOX.csv',
'flDPnn': 'processed_data/caid2_competition_results/flDPnn_CAID-2_Disorder_NOX.csv',
'flDPlr': 'processed_data/caid2_competition_results/flDPtr_CAID-2_Disorder_NOX.csv', # name doesn't match but this is what it is in raw download
'flDPlr2': 'processed_data/caid2_competition_results/flDPlr2_CAID-2_Disorder_NOX.csv',
'DisoPred': 'processed_data/caid2_competition_results/DisoPred_CAID-2_Disorder_NOX.csv',
'IDP-Fusion': 'processed_data/caid2_competition_results/IDP-Fusion_CAID-2_Disorder_NOX.csv',
'ESpritz-D': 'processed_data/caid2_competition_results/ESpritz-D_CAID-2_Disorder_NOX.csv',
'DeepIDP-2L': 'processed_data/caid2_competition_results/DeepIDP-2L_CAID-2_Disorder_NOX.csv',
'disomine': 'processed_data/caid2_competition_results/disomine_CAID-2_Disorder_NOX.csv',
'DISOPRED3-diso': 'processed_data/caid2_competition_results/DISOPRED3-diso_CAID-2_Disorder_NOX.csv',
'AlphaFold-rsa': 'processed_data/caid2_competition_results/AlphaFold-rsa_CAID-2_Disorder_NOX.csv',
'AlphaFold-pLDDT': 'processed_data/caid2_competition_results/AlphaFold-disorder_CAID-2_Disorder_NOX.csv', # name doesn't match but this is what it is in raw download
'IUPred3': 'processed_data/caid2_competition_results/IUPred3_CAID-2_Disorder_NOX.csv',
model_alias: path_to_results_of_interest
}
if path_to_esm_results is not None:
method_results['ESM-2-650M'] = path_to_esm_results
color_map['ESM-2-650M'] = 'black'
method_results = {k:v for k,v in method_results.items() if v not in [None, '']}
set_font()
plt.figure(figsize=(12,6),dpi=300)
# To store AUROC values and corresponding labels for sorting
merged_preds = pd.DataFrame(data={'sequence':[]})
merged_tpr_fpr = pd.DataFrame(data={'model': [],'fpr':[],'tpr':[]})
roc_data = []
# Read each result file and plot the metrics
for method, path in method_results.items():
df = pd.read_csv(path) # columns = prob_1,labels
merged_preds = pd.merge(merged_preds,
df.rename(columns={'prob_1':f"{method}_prob_1"})[['sequence',f"{method}_prob_1",]],
on=['sequence'],how='outer')
# Extract probabilities and labels
prob_1 = ",".join(df['prob_1'].tolist())
df['labels'] = df['sequence'].apply(lambda x: seq_label_dict[x])
labels = "".join(df['labels'].tolist())
prob_1 = [float(x) for x in prob_1.split(",")]
labels = [int(x) for x in list(labels)]
sequences = "".join(df['sequence'].tolist())
assert len(prob_1)==len(labels)==len(sequences)
# Compute ROC curve and ROC area
fpr, tpr, thresholds = roc_curve(labels, prob_1)
new_tpr_fpr = pd.DataFrame(data={
'model': [method]*len(fpr),
'fpr': fpr, 'tpr': tpr
})
merged_tpr_fpr = pd.concat([merged_tpr_fpr,new_tpr_fpr])
roc_auc = auc(fpr, tpr)
if method==model_alias:
path_to_og_metrics = path_to_results_of_interest.rsplit('/',1)[0]+'/caid_hyperparam_screen_test_metrics.csv'
og_metrics = pd.read_csv(path_to_og_metrics)
roc_auc = og_metrics['AUROC'][0]
# Store data for sorting later
roc_data.append((method, fpr, tpr, roc_auc))
# Save the merged dataframe as source data
merged_preds['labels'] = merged_preds['sequence'].apply(lambda x: seq_label_dict[x])
merged_preds['labels'] = merged_preds['labels'].apply(lambda x: ",".join([str(y) for y in x]))
merged_preds['ids'] = merged_preds['sequence'].apply(lambda x: seq_ids_dict[x])
merged_preds.drop(columns={'sequence'}).to_csv(f"{results_dir}/CAID_prediction_source_data.csv",index=False)
merged_tpr_fpr.to_csv(f"{results_dir}/CAID_fpr_tpr_source_data.csv",index=False)
# Sort the methods by AUROC values
roc_data = sorted(roc_data, key=lambda x: x[3], reverse=True)
# figure out the labels
labels = {method: method for method in method_results}
if with_rankings:
for method in labels:
if method in caid2_model_rankings:
labels[method] = f"{caid2_model_rankings[method]}. {method}"
# Plot sorted ROC curves
for method, fpr, tpr, roc_auc in roc_data:
if method=='ESM-2-650M' and path_to_esm_results is not None:
plt.plot(fpr, tpr, color=color_map[method], lw=2, linestyle='--', label=f'{labels[method]} ({roc_auc:0.3f})')
elif method == model_alias:
plt.plot(fpr, tpr, color=color_map[method], lw=2, label=f'{labels[method]} ({roc_auc:0.3f})')
else:
plt.plot(fpr, tpr, color=color_map[method], lw=1, alpha=0.7, label=f'{labels[method]} ({roc_auc:0.3f})')
# Set other stylistic elements
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.plot([0, 1], [0, 1], color='darkgrey', lw=2, linestyle='--')
plt.xlabel('False Positive Rate', fontsize=22)
plt.ylabel('True Positive Rate', fontsize=22)
plt.title('CAID2 Disorder NOX Dataset: ROC Curve', fontsize=22)
# After plotting the ROC curves, customize the legend
handles, labels = plt.gca().get_legend_handles_labels()
# Create the legend first
legend = plt.legend(handles, labels, loc="center left", bbox_to_anchor=(1.1, 0.5), fontsize=16)
# Iterate through the legend's text labels
for text in legend.get_texts():
if model_alias in text.get_text():
text.set_fontweight('bold') # Bold the alias model
elif (path_to_esm_results is not None) and "ESM-2-650M" in text.get_text():
text.set_fontweight('bold') # Bold ESM if we're comparing to it
plt.tight_layout()
figpath = f'{results_dir}/CAID2_{model_alias}_AUROC_curve.png'
if path_to_esm_results is not None:
figpath = f'{results_dir}/CAID2_{model_alias}_with_ESM_AUROC_curve.png'
plt.savefig(figpath)
def plot_disorder_content_scatter(train_labels, test_labels, benchmark_labels, savepath='splits/disorder_content_scatter.png'):
"""
Compare disorder content between the train, test, and fusion benchmark sets based on the TRUE labels.
Each labels vector should have ['11110000','0001110',...] format.
"""
# Get train disorder distribution
train_lengths = []
train_frac_disorder = []
for vec in train_labels:
veclist = [int(x) for x in vec]
train_lengths.append(len(veclist))
train_frac_disorder.append(sum(veclist)/len(veclist))
# Get test disorder distribution
test_lengths = []
test_frac_disorder = []
for vec in test_labels:
veclist = [int(x) for x in vec]
test_lengths.append(len(veclist))
test_frac_disorder.append(sum(veclist)/len(veclist))
# Get benchmark disorder distribution
benchmark_lengths = []
benchmark_frac_disorder = []
for vec in benchmark_labels:
veclist = [int(x) for x in vec]
benchmark_lengths.append(len(veclist))
benchmark_frac_disorder.append(sum(veclist)/len(veclist))
# make a plot
set_font()
color_map = {
'train': '#0072B2',
'test': '#E69F00',
'fusion': 'purple'
}
# Plotting
fig, ax = plt.subplots(figsize=(10, 6))
ax.scatter(train_lengths, train_frac_disorder, color=color_map['train'], label='Train', alpha=0.7)
ax.scatter(test_lengths, test_frac_disorder, color=color_map['test'], label='Test', alpha=0.7)
ax.scatter(benchmark_lengths, benchmark_frac_disorder, color=color_map['fusion'], label='Fusion', alpha=0.7)
# Labels and title
ax.set_xlabel('Length')
ax.set_ylabel('Fraction of Disorder')
ax.set_title('Length vs. Fraction of Disorder for Train, Test, and Benchmark Datasets')
ax.legend()
plt.tight_layout()
plt.savefig(savepath)
def plot_disorder_content_hist(labels, ids, title="data", color="black", savepath='splits/disorder_content_histograms.png'):
"""
Compare disorder content between the train, test, and fusion benchmark sets based on the TRUE labels.
Each labels vector should have ['11110000','0001110',...] format.
"""
set_font()
# Get disorder distribution
lengths = []
frac_disorder = []
for vec in labels:
veclist = [int(x) for x in vec]
lengths.append(len(veclist))
frac_disorder.append(100*sum(veclist)/len(veclist)) # make it a percent, i like this better
# save the source data
source_data = pd.DataFrame(data={
'ID': ids,
'Percent_Disordered': frac_disorder
})
source_data['Percent_Disordered'] = source_data['Percent_Disordered'].round(3)
source_data.to_csv(savepath.replace(".png","_source_data.csv"),index=False)
fig, ax = plt.subplots(1, 1, figsize=(20, 12))
# Plot histogram for train data
title_fontsize = 70
axislabel_fontsize = 70
tick_fontsize = 50
ax.hist(frac_disorder, bins=20, color=color, alpha=0.7)
ax.set_title(title, fontsize=title_fontsize)
ax.set_xlabel('% Disordered', fontsize=axislabel_fontsize)
ax.set_ylabel('Count', fontsize=axislabel_fontsize)
ax.grid(True)
ax.set_axisbelow(True)
ax.tick_params(axis='both', which='major', labelsize=tick_fontsize)
# Calculate the mean and median of the percent coverage
mean_coverage = np.mean(frac_disorder)
median_coverage = np.median(frac_disorder)
# Add vertical line for the mean
ax.axvline(mean_coverage, color='black', linestyle='--', linewidth=2, label=f'Mean: {mean_coverage:.1f}%')
# Add vertical line for the median
ax.axvline(median_coverage, color='black', linestyle='-', linewidth=2, label=f'Median: {median_coverage:.1f}%')
ax.legend(fontsize=50, title_fontsize=50)
plt.tight_layout()
plt.savefig(savepath)
def plot_group_disorder_content_hist(train_labels, test_labels, benchmark_labels, savepath='splits/disorder_content_histograms.png',orient='horizontal'):
"""
Compare disorder content between the train, test, and fusion benchmark sets based on the TRUE labels.
Each labels vector should have ['11110000','0001110',...] format.
"""
# Get train disorder distribution
train_lengths = []
train_frac_disorder = []
for vec in train_labels:
veclist = [int(x) for x in vec]
train_lengths.append(len(veclist))
train_frac_disorder.append(sum(veclist)/len(veclist))
# Get test disorder distribution
test_lengths = []
test_frac_disorder = []
for vec in test_labels:
veclist = [int(x) for x in vec]
test_lengths.append(len(veclist))
test_frac_disorder.append(sum(veclist)/len(veclist))
# Get benchmark disorder distribution
benchmark_lengths = []
benchmark_frac_disorder = []
for vec in benchmark_labels:
veclist = [int(x) for x in vec]
benchmark_lengths.append(len(veclist))
benchmark_frac_disorder.append(sum(veclist)/len(veclist))
# make a plot
set_font()
color_map = {
'train': '#0072B2',
'test': '#E69F00',
'fusion': 'mediumpurple'
}
# Create a 1x3 subplot (1 row, 3 columns) or 3x1
if orient=='horizontal':
fig, axes = plt.subplots(1, 3, figsize=(15, 5), sharey=False)
if orient=='vertical':
fig, axes = plt.subplots(3, 1, figsize=(5, 15), sharey=False)
# Plot histogram for train data
title_fontsize = 26
axislabel_fontsize = 26
tick_fontsize = 16
axes[0].hist(train_frac_disorder, bins=20, color=color_map['train'], alpha=0.7)
axes[0].set_title('CAID2 Train', fontsize=title_fontsize)
if orient=="horizontal":
axes[0].set_xlabel('Fraction of Disorder', fontsize=axislabel_fontsize)
axes[0].set_ylabel('Frequency', fontsize=axislabel_fontsize)
axes[0].grid(True)
axes[0].set_axisbelow(True)
axes[0].tick_params(axis='both', which='major', labelsize=tick_fontsize)
# Plot histogram for test data
axes[1].hist(test_frac_disorder, bins=20, color=color_map['test'], alpha=0.7)
axes[1].set_title('CAID2 Test',fontsize=title_fontsize)
if orient=="horizontal":
axes[1].set_xlabel('Fraction of Disorder', fontsize=axislabel_fontsize)
if orient=="vertical":
axes[1].set_ylabel('Frequency', fontsize=axislabel_fontsize)
axes[1].grid(True)
axes[1].set_axisbelow(True)
axes[1].tick_params(axis='both', which='major', labelsize=tick_fontsize)
# Plot histogram for benchmark (fusion) data
axes[2].hist(benchmark_frac_disorder, bins=20, color=color_map['fusion'], alpha=0.7)
axes[2].set_title('Fusion Oncoproteins',fontsize=title_fontsize)
axes[2].set_xlabel('Fraction of Disorder', fontsize=axislabel_fontsize)
if orient=="vertical":
axes[2].set_ylabel('Frequency', fontsize=axislabel_fontsize)
axes[2].grid(True)
axes[2].set_axisbelow(True)
axes[2].tick_params(axis='both', which='major', labelsize=tick_fontsize)
plt.tight_layout()
plt.savefig(savepath)
def categorize_plddt(values):
categories = {
"<= 50": sum(1 for x in values if x <= 50),
"50-70": sum(1 for x in values if 50 < x <= 70),
"70-90": sum(1 for x in values if 70 < x <= 90),
"> 90": sum(1 for x in values if x > 90)
}
return categories
def plot_fusion_sequence_pLDDT_left_to_right(fusion_structure_data, fusiongene, save_path=''):
"""
Plot each amino acid in the sequence as a separate colored bar based on pLDDT values.
"""
set_font()
# Filter for specific fusion data and preprocess
df_of_interest = fusion_structure_data[fusion_structure_data['FusionGene'] == fusiongene].copy()
df_of_interest['Fusion_AA_pLDDTs'] = df_of_interest['Fusion_AA_pLDDTs'].apply(lambda x: [float(i) for i in x.split(',')])
df_of_interest['Label'] = df_of_interest['Fusion_Length'].astype(str) + 'AAs'
# Sort data by Fusion_Length
df_of_interest = df_of_interest.sort_values(by='Fusion_Length', ascending=True).reset_index(drop=True)
# Define colors for each pLDDT range
category_colors = {"<= 50": "#f27842", "50-70": "#f8d514", "70-90": "#60c1e8", "> 90": "#004ecb"}
# Helper function to get color based on pLDDT
def get_color(pLDDT):
if pLDDT > 90:
return category_colors["> 90"]
elif pLDDT > 70:
return category_colors["70-90"]
elif pLDDT > 50:
return category_colors["50-70"]
else:
return category_colors["<= 50"]
# Start plotting each sequence with colored bars
fig, ax = plt.subplots(figsize=(10, 6))
if len(df_of_interest)<3:
fig, ax = plt.subplots(figsize=(10, 2))
average_plddt = dict(zip(df_of_interest['Label'], df_of_interest['Fusion_pLDDT']))
df_of_interest['Fusion_AA_colors'] = df_of_interest['Fusion_AA_pLDDTs'].apply(lambda x: [get_color(plddt) for plddt in x])
df_of_interest['Fusion_pLDDT_color'] = df_of_interest['Fusion_pLDDT'].apply(lambda plddt: get_color(plddt))
# just save the columns needed for the plot
df_of_interest[['FusionGene','seq_id','Fusion_Length','Fusion_pLDDT','Fusion_AA_pLDDTs','Fusion_AA_colors','Fusion_pLDDT_color',
'top_hg_UniProtID','top_hg_UniProt_isoform','top_hg_UniProt_fus_indices',
'top_tg_UniProtID','top_tg_UniProt_isoform','top_tg_UniProt_fus_indices']].to_csv(f"{save_path}/plddt_sequence_{fusiongene}_source_data.csv",index=False)
for idx, row in df_of_interest.iterrows():
pLDDT_values = row['Fusion_AA_pLDDTs']
colors = [get_color(plddt) for plddt in pLDDT_values]
# Plot each amino acid in the sequence with the respective color
ax.bar(range(len(pLDDT_values)),
[0.7] * len(pLDDT_values), color=colors, edgecolor='none',
bottom=idx - 0.7 / 2) # Centering each row at idx
labels = df_of_interest['Label'].tolist()
# Annotate each bar with the Fusion_pLDDT value on the right, colored by PLDDT category
for idx, label in enumerate(labels):
avg_plddt_value = average_plddt[label]
# Determine color based on the PLDDT category
if avg_plddt_value > 90:
color = '#004ecb'
elif avg_plddt_value > 70:
color = "#60c1e8"
elif avg_plddt_value > 50:
color = '#f8d514'
else:
color = '#f27842'
# Annotate with the determined color
if len(df_of_interest)>10:
markersize = 10
elif len(df_of_interest)>5:
markersize = 16
else:
markersize=12
ax.plot(1.02*max(df_of_interest['Fusion_Length']),
idx, marker='o', color="black", markersize=markersize, markerfacecolor=color, markeredgewidth=2)
# Add breakpoint box - make sure we actually HAVE one of each
hg_indices, tg_indices = None, None
if not(type(df_of_interest['top_hg_UniProt_fus_indices'][idx])==float):
hg_indices = [int(x) for x in df_of_interest['top_hg_UniProt_fus_indices'][idx].split(',')]
if not(type(df_of_interest['top_tg_UniProt_fus_indices'][idx])==float):
tg_indices = [int(x) for x in df_of_interest['top_tg_UniProt_fus_indices'][idx].split(',')]
print(hg_indices, tg_indices)
if (hg_indices is not None) and (tg_indices is not None):
box_start = min(hg_indices[-1],tg_indices[0])
box_end = max(hg_indices[-1],tg_indices[0])
elif hg_indices is not None:
box_start, box_end = hg_indices[-1], hg_indices[-1]
elif tg_indices is not None:
box_start, box_end = tg_indices[0], tg_indices[0]
print(f"box indices for structure {idx}, fusion gene {fusiongene}", box_start, box_end)
# Plot the rectangle, making it slightly larger than the rest of the bar
rect = patches.Rectangle((box_start, idx - 0.7 / 2), box_end-box_start, 0.7, linewidth=2, edgecolor='black', facecolor='none')
ax.add_patch(rect)
# Customize plot
ax.set_yticks([]) # Hide y-axis ticks
ax.set_yticklabels([]) # Hide y-axis labels
ax.set_ylim(-0.5, len(df_of_interest) - 0.5) # reduce white space at top
ax.set_xlabel("Amino Acid Sequence (ordered)", fontsize=14)
# Customize x-axis for labeling
ax.set_xlim(left=0) # Start x-axis at 0 to make bars flush left
ax.set_xlabel("Amino Acid Sequence (ordered)", fontsize=14)
ax.tick_params(axis='x', labelsize=30)
plt.title(f"{fusiongene} pLDDT Distribution by Amino Acid Sequence", fontsize=16)
plt.tight_layout()
# Save figure
fusiongene_savename = fusiongene.replace("::","-")
plt.savefig(f"{save_path}/plddt_sequence_{fusiongene_savename}.png", dpi=300)
plt.show()
def plot_favorite_fusion_pLDDT_distribution(fusion_structure_data, fusiongene, save_path=''):
"""
Make a stacked bar chart of the pLDDT distribution
"""
set_font()
# Filter for EWSR1::FLI1 fusion data and preprocess
df_of_interest = fusion_structure_data[fusion_structure_data['FusionGene'] == fusiongene].copy()
df_of_interest['Fusion_AA_pLDDTs'] = df_of_interest['Fusion_AA_pLDDTs'].apply(lambda x: [float(i) for i in x.split(',')])
df_of_interest['Label'] = df_of_interest['Fusion_Length'].astype(str) + 'AAs'
# Sort data by Fusion_Length
df_of_interest = df_of_interest.sort_values(by='Fusion_Length', ascending=True).reset_index(drop=True)
# Convert to dictionary format
data_dict = dict(zip(df_of_interest['Label'], df_of_interest['Fusion_AA_pLDDTs']))
average_plddt = dict(zip(df_of_interest['Label'], df_of_interest['Fusion_pLDDT']))
# Categorize each structure
categorized_data = {structure: categorize_plddt(plddt_values) for structure, plddt_values in data_dict.items()}
# Extract counts for each category
labels = list(categorized_data.keys())
categories = ["<= 50", "50-70", "70-90", "> 90"]
counts = {cat: [categorized_data[structure][cat] for structure in labels] for cat in categories}
# Define colors for each category
category_colors = {"<= 50": "#f27842", "50-70": "#f8d514", "70-90": "#60c1e8", "> 90": "#004ecb"}
# Re-categorize PLDDT values for the bar chart
categorized_data = {structure: categorize_plddt(plddt_values) for structure, plddt_values in data_dict.items()}
labels = list(categorized_data.keys())
counts = {cat: [categorized_data[structure][cat] for structure in labels] for cat in categories}
# Plotting the horizontal stacked bar chart with annotations for 'Fusion_pLDDT' values
fig, ax = plt.subplots(figsize=(10, 6))
if len(data_dict)<3:
fig, ax = plt.subplots(figsize=(10, 2))
bottom = np.zeros(len(labels))
# Stack each category horizontally
for cat in categories:
ax.barh(labels, counts[cat], label=cat, color=category_colors[cat], left=bottom)
bottom += counts[cat] # Update the left position for the next stack
# Annotate each bar with the Fusion_pLDDT value on the right, colored by PLDDT category
for idx, label in enumerate(labels):
avg_plddt_value = average_plddt[label]
# Determine color based on the PLDDT category
if avg_plddt_value > 90:
color = '#004ecb'
elif avg_plddt_value > 70:
color = "#60c1e8"
elif avg_plddt_value > 50:
color = '#f8d514'
else:
color = '#f27842'
# Annotate with the determined color
#ax.text(bottom[idx] + 1, idx, f"{avg_plddt_value:.2f}", va='center', ha='left', color="black", fontsize=18, fontweight='bold')
if len(df_of_interest)>10:
markersize = 10
elif len(df_of_interest)>5:
markersize = 16
else:
markersize=12
ax.plot(bottom[idx] + .02*max(df_of_interest['Fusion_Length']), idx, marker='s', color="black", markersize=markersize, markerfacecolor=color, markeredgewidth=2)
# Add labels and legend
#ax.set_xlim([0,max(df_of_interest['Fusion_Length'])*1.0])
#ax.set_ylabel("Structures")
# Save original ticks before changing label size
#ax.tick_params(axis='x', labelsize=16)
#original_xticks = ax.get_xticks()
# Set ticks explicitly to avoid automatic adjustment
#ax.set_xticks(original_xticks)
#ax.set_xlabel("Length",fontsize=40)
ax.tick_params(axis='x', labelsize=30)
#ax.tick_params(axis='y', labelsize=16)
ax.tick_params(axis='y', left=False, labelleft=False)
#ax.set_title(f"{fusiongene} pLDDT Distribution")
#ax.legend(title="pLDDT Ranges", fontsize=16, bbox_to_anchor=(1, 1), title_fontsize=16)
plt.tight_layout()
fusiongene_savename = fusiongene.replace("::","-")
plt.savefig(f"{save_path}/plddt_dist_{fusiongene_savename}.png",dpi=300)
def make_all_favorite_fusion_pLDDT_plots(favorite_fusions,left_to_right=True):
fusion_structure_data = pd.read_csv('processed_data/fusionpdb/FusionPDB_level2-3_cleaned_structure_info.csv')
swissprot_top_alignments = pd.read_csv("../../data/blast/blast_outputs/swissprot_top_alignments.csv")
fuson_db = pd.read_csv("../../data/fuson_db.csv")
seq_id_dict = dict(zip(fuson_db['aa_seq'],fuson_db['seq_id']))
fusion_structure_data['seq_id'] = fusion_structure_data['Fusion_Seq'].map(seq_id_dict)
fusion_structure_data = pd.merge(
fusion_structure_data,
swissprot_top_alignments,
on="seq_id",
how="left"
)
for x in favorite_fusions:
if left_to_right:
plot_fusion_sequence_pLDDT_left_to_right(fusion_structure_data, x, save_path='processed_data/figures/fusion_disorder')
else:
plot_favorite_fusion_pLDDT_distribution(fusion_structure_data, x, save_path='processed_data/figures/fusion_disorder')
def prep_data_for_ht_disorder_comparison():
ht_structure_data = pd.read_csv('processed_data/fusionpdb/heads_tails_structural_data.csv')
fusion_structure_data = pd.read_csv('processed_data/fusionpdb/FusionPDB_level2-3_cleaned_structure_info.csv')
fusion_heads_and_tails = pd.read_csv('processed_data/fusionpdb/fusion_heads_and_tails.csv')
all_hts_with_structures = ht_structure_data['UniProtID'].unique().tolist()
fuson_ht_db = pd.read_csv('../../data/blast/fuson_ht_db.csv')[['seq_id','aa_seq','fusiongenes','hgUniProt','tgUniProt']]
merge = pd.merge(
fuson_ht_db.rename(columns={'aa_seq':'Fusion_Seq'}),
fusion_structure_data[['FusionGID', 'Fusion_Seq','Fusion_pLDDT','Fusion_AA_pLDDTs']],
on='Fusion_Seq',
how='right'
)
# now merge again
merge['hgUniProt'] = merge['hgUniProt'].apply(lambda x: x.split(','))
merge['tgUniProt'] = merge['tgUniProt'].apply(lambda x: x.split(','))
merge = merge.explode('hgUniProt')
merge = merge.explode('tgUniProt')
merge = merge.loc[
merge['hgUniProt'].isin(all_hts_with_structures) &
merge['tgUniProt'].isin(all_hts_with_structures)
].reset_index(drop=True)
merge = pd.merge(
merge,
ht_structure_data.rename(columns=
{'UniProtID':'hgUniProt',
'Avg pLDDT': 'hg_pLDDT',
'All pLDDTs': 'hg_AA_pLDDTs',
'Seq': 'hg_seq'}),
on='hgUniProt',
how='inner'
)
merge = pd.merge(
merge,
ht_structure_data.rename(columns=
{'UniProtID':'tgUniProt',
'Avg pLDDT': 'tg_pLDDT',
'All pLDDTs': 'tg_AA_pLDDTs',
'Seq': 'tg_seq'}),
on='tgUniProt',
how='inner'
)
merge = merge.loc[merge['hg_AA_pLDDTs'].notna()]
merge = merge.loc[merge['tg_AA_pLDDTs'].notna()].reset_index(drop=True)
# finally, calcualte label
merge['hg_label'] = merge['hg_AA_pLDDTs'].apply(lambda x: x.split(','))
merge['hg_label'] = merge['hg_label'].apply(lambda x: [float(y) for y in x])
merge['hg_label'] = merge['hg_label'].apply(lambda x: [apply_plddt_thresh(y) for y in x])
merge['hg_label'] = merge['hg_label'].apply(lambda x: ''.join(x))
merge['tg_label'] = merge['tg_AA_pLDDTs'].apply(lambda x: x.split(','))
merge['tg_label'] = merge['tg_label'].apply(lambda x: [float(y) for y in x])
merge['tg_label'] = merge['tg_label'].apply(lambda x: [apply_plddt_thresh(y) for y in x])
merge['tg_label'] = merge['tg_label'].apply(lambda x: ''.join(x))
merge['fusion_label'] = merge['Fusion_AA_pLDDTs'].apply(lambda x: x.split(','))
merge['fusion_label'] = merge['fusion_label'].apply(lambda x: [float(y) for y in x])
merge['fusion_label'] = merge['fusion_label'].apply(lambda x: [apply_plddt_thresh(y) for y in x])
merge['fusion_label'] = merge['fusion_label'].apply(lambda x: ''.join(x))
return merge
def apply_plddt_thresh(y):
if y < 68.8:
return '1'
else:
return '0'
def plot_fusion_stats_boxplots(data, save_path="fusion_disorder_boxplots.png"):
set_font()
# Create box plots
plt.figure(figsize=(6, 5))
# for ones that are 100% disordered, AUROC was NaN, so drop these
box = plt.boxplot([data[col].dropna() for col in data.columns], labels=data.columns, patch_artist=True)
# Set color of each box plot
for patch in box['boxes']:
patch.set_facecolor('#ff68b4')
patch.set_edgecolor('#ff68b4')
# Customize other elements if needed
#for whisker in box['whiskers']:
#whisker.set_color('#ff68b4')
#for cap in box['caps']:
#cap.set_color('#ff68b4')
for median in box['medians']:
median.set_color('black')
# Add labels and title
#plt.xlabel('Metrics')
#plt.ylabel('Values')
plt.title(f"Per-Residue Disorder (n={len(data)})",fontsize=22)
plt.xticks(rotation=20,fontsize=22)
plt.yticks(fontsize=22)
# Show plot
plt.tight_layout()
plt.show()
plt.savefig(save_path,dpi=300)
def plot_fusion_frac_disorder_r2(actual_values, predicted_values, save_path="fusion_pred_disorder_r2.png"):
set_font()
plt.figure(figsize=(6, 6))
r2 = r2_score(actual_values, predicted_values)
#sns.kdeplot(actual_values, label="Actual Values", shade=True)
#sns.kdeplot(predicted_values, label="Predicted Values", shade=True)
plt.scatter(actual_values, predicted_values, alpha=0.5, label=f"Predictions", color="#ff68b4")
plt.plot([min(actual_values), max(actual_values)], [min(actual_values), max(actual_values)], 'k--', label='Ideal Fit')
plt.text(0, 92, f"$R^2$={r2:.2f}", fontsize=32)
# Adjusting font sizes and setting font properties
plt.xlabel(f'AlphaFold-pLDDT',size=32)
plt.ylabel(f'FusOn-pLM-Diso',size=32)
plt.title(f"% Disordered (n={len(actual_values)})",size=32)
plt.xticks(fontsize=24)
plt.yticks(fontsize=24)
#plt.xlabel("Values")
#plt.ylabel("Density")
#plt.title(f"Density Plot of Actual vs Predicted Values (R^2 = {r2:.2f})")
plt.legend(prop={'size': 16})
plt.tight_layout()
plt.show()
plt.savefig(save_path, dpi=300)
def main():
set_font()
#output_dir = "results/test"
output_dir = "results/final"
seq_label_dict = pd.read_csv('splits/test_df.csv')
seq_ids_dict = dict(zip(seq_label_dict['Sequence'],seq_label_dict['IDs']))
seq_label_dict = dict(zip(seq_label_dict['Sequence'],seq_label_dict['Label']))
best_caid_model_results = pd.read_csv(f"{output_dir}/best_caid_model_results.csv")
make_auroc_curve(results_dir=output_dir,
seq_label_dict=seq_label_dict,
seq_ids_dict=seq_ids_dict,
path_to_results_of_interest="trained_models/fuson_plm/best/caid_hyperparam_screen_test_probs.csv",
model_alias="FusOn-pLM",
path_to_esm_results="trained_models/esm2_t33_650M_UR50D/best/caid_hyperparam_screen_test_probs.csv",
with_rankings=True)
caid2_test_data = pd.read_csv(f"splits/splits.csv")
caid2_test_data = caid2_test_data.loc[caid2_test_data['Split']=='Test']
caid2_test_labels = caid2_test_data['Label'].tolist()
caid2_test_ids = caid2_test_data['IDs'].tolist()
# fusions, heads, and tails
fusion_ht_data = prep_data_for_ht_disorder_comparison()
os.makedirs("processed_data/figures",exist_ok=True)
head_data = fusion_ht_data.drop_duplicates(['hg_seq']).reset_index(drop=True)
head_labels = head_data['hg_label'].tolist()
head_ids = head_data['hgUniProt'].tolist()
tail_data = fusion_ht_data.drop_duplicates(['tg_seq']).reset_index(drop=True)
tail_labels = tail_data['tg_label'].tolist()
tail_ids = tail_data['tgUniProt'].tolist()
fusion_data = fusion_ht_data.drop_duplicates(['Fusion_Seq']).reset_index(drop=True)
fusion_labels = fusion_data['fusion_label'].tolist()
fusion_ids = fusion_data['seq_id'].tolist()
plt.rc('text', usetex=False)
math_part = r"$n$"
os.makedirs("processed_data/figures/histograms",exist_ok=True)
plot_disorder_content_hist(caid2_test_labels, caid2_test_ids, title=f"CAID2 Disorder-NOX ({math_part}={len(caid2_test_labels):,})", color="black", savepath='processed_data/figures/histograms/disorder_nox_histogram.png')
plot_disorder_content_hist(head_labels, head_ids, title=f"Head Proteins ({math_part}={len(head_labels):,})", color="#df8385", savepath='processed_data/figures/histograms/heads_histogram.png')
plot_disorder_content_hist(tail_labels, tail_ids, title=f"Tail Proteins ({math_part}={len(tail_labels):,})", color="#6ea4da", savepath='processed_data/figures/histograms/tails_histogram.png')
plot_disorder_content_hist(fusion_labels, fusion_ids, title=f"Fusion Oncoproteins ({math_part}={len(fusion_labels):,})", color="mediumpurple", savepath='processed_data/figures/histograms/fusions_histogram.png')
os.makedirs("processed_data/figures/fusion_disorder",exist_ok=True)
make_all_favorite_fusion_pLDDT_plots([
"EWSR1::FLI1",
"PAX3::FOXO1",
"EML4::ALK",
"SS18::SSX1"],
left_to_right=True)
if __name__ == "__main__":
main() |