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import pandas as pd
import bokeh.io
import bokeh.plotting
import bokeh.palettes
from bokeh.transform import factor_cmap
import datashader
import holoviews as hv
import holoviews.operation.datashader
import os
from natsort import natsorted
hv.extension("bokeh")
import warnings
# Suppress FutureWarning messages
warnings.simplefilter(action='ignore',)
cr9114_dict = {
'ab_name' : 'CR9114',
'files' : {
'ESM-IF1': 'output/ab_mutagenesis_expts/cr9114/4fqi_ablh_scores.csv',
'ProteinMPNN': 'output/ab_mutagenesis_expts/cr9114/mpnn/score_only/',
},
'dms_df' : pd.read_csv('data/ab_mutagenesis_expts/cr9114/cr9114_exp_data.csv',
dtype = {'genotype': str},
).rename(columns={'h1_mean': 'H1', 'h3_mean' : 'H3'}),
'ag_columns': ['H1', 'H3'],
'expt_type': 'Combinatorial Mutagenesis for Affinity',
'palette': bokeh.palettes.Spectral6
}
cr6261_dict = {
'ab_name' : 'CR6261',
'files' : {
'ESM-IF1': 'output/ab_mutagenesis_expts/cr6261/3gbn_ablh_scores.csv',
'ProteinMPNN': 'output/ab_mutagenesis_expts/cr6261/mpnn/score_only/'
},
'dms_df' : pd.read_csv('data/ab_mutagenesis_expts/cr6261/cr6261_exp_data.csv',
dtype={'genotype': str},
).rename(columns={'h1_mean': 'H1', 'h9_mean' : 'H9'}),
'ag_columns': ['H1', 'H9'],
'expt_type': 'Combinatorial Mutagenesis for Affinity',
'palette': bokeh.palettes.Dark2_6,
}
g6LC_dict = {
'ab_name' : 'g6',
'files' : {
'ESM-IF1':'output/ab_mutagenesis_expts/g6/2fjg_vlh_lc_scores.csv',
'ProteinMPNN':'output/ab_mutagenesis_expts/g6/proteinMpnnLC/score_only/',
},
'dms_df' : pd.read_csv('data/ab_mutagenesis_expts/g6/g6_lc_exp_data.csv'),
'ag_columns': ['norm_binding'],
'expt_type': 'Deep Mutational Scan for Binding',
'palette': bokeh.palettes.Pastel1_6,
'chain': 'VL'
}
g6HC_dict = {
'ab_name' : 'g6',
'files' : {
'ESM-IF1': 'output/ab_mutagenesis_expts/g6/2fjg_vlh_hc_scores.csv',
'ProteinMPNN':'output/ab_mutagenesis_expts/g6/proteinMpnnHC/score_only/',
},
'dms_df' : pd.read_csv('data/ab_mutagenesis_expts/g6/g6_hc_exp_data.csv'),
'ag_columns': ['norm_binding'],
'expt_type': 'Deep Mutational Scan for Binding',
'palette': bokeh.palettes.Pastel1_4,
'chain': 'VH'
}
def combine_mpnn_scores(dir):
global_scores = []
npz_files = [f for f in os.listdir(dir) if f.endswith('.npz')]
npz_files = natsorted(npz_files)[:-1]
for fname in npz_files:
file_path = os.path.join(dir, fname)
data = np.load(file_path)
global_scores.append(-1* data['global_score'][0])
data.close()
return global_scores
#get melted correlations matrix of structure input x target antigen, for a given antibody
def get_corr(ab_name, files, dms_df, ag_columns,):
#retreive correlations from inverse fold scores
for key, filepath in files.items():
column_label = key
if key == 'ProteinMPNN':
scores = combine_mpnn_scores(filepath)
dms_df[column_label] = scores
elif key == 'ESM-IF1':
df = pd.read_csv(files['ESM-IF1']) # Read the CSV file into a DataFrame
log_likelihood_column = df['log_likelihood'] # Extract the 'log_likelihood' column
dms_df[column_label] = log_likelihood_column
conditions = list(files.keys())
correlations = dms_df[ag_columns + conditions].corr(method='spearman')
correlations = correlations.drop(conditions, axis= 1)
correlations = correlations.drop(ag_columns, axis= 0)
# Melt the correlations DataFrame and rename the index column
melted_correlations = (
correlations
.reset_index()
.melt(id_vars='index', var_name='Target', value_name='Correlation')
.rename(columns={'index': 'Input'})
)
print(melted_correlations)
return melted_correlations
return all_ag_melted
#get melted correlations matrix of structure input x target antigen, for g6 antibody
def get_g6_corr(g6Hc_dict, g6LC_dict, dropLOQ = False ):
ab_name = 'g6'
ag_columns = g6LC_dict['ag_columns']
vh_and_vl_dms_df = pd.DataFrame({})
for d in [g6HC_dict, g6LC_dict]:
files = d['files']
dms_df = d['dms_df']
#retreive correlations from abysis, ablang, and esm1v scores
for key, filepath in files.items():
column_label = key
if key == 'ProteinMPNN':
scores = combine_mpnn_scores(filepath)
dms_df[column_label] = scores
elif key == 'ESM-IF1':
df = pd.read_csv(files['ESM-IF1']) # Read the CSV file into a DataFrame
log_likelihood_column = df['log_likelihood'] # Extract the 'log_likelihood' column
dms_df[column_label] = log_likelihood_column
vh_and_vl_dms_df = pd.concat([vh_and_vl_dms_df, dms_df], ignore_index= True)
conditions = list(files.keys())
correlations = vh_and_vl_dms_df[ag_columns + conditions].corr(method='spearman')
correlations = correlations.drop(conditions, axis= 1)
correlations = correlations.drop(ag_columns, axis= 0)
# Melt the correlations DataFrame and rename the index column
melted_correlations = (
correlations
.reset_index()
.melt(id_vars='index', var_name='Target', value_name='Correlation')
.rename(columns={'index': 'Input'})
)
melted_correlations['Target'] = ['VEGF-A'] * len(melted_correlations)
print(melted_correlations)
return melted_correlations
#plot correlation bars for cr antibodies: cr6261 and cr9114
def plot_hbar(title, melted_correlations, palette = bokeh.palettes.Spectral6, ):
melted_correlations['cats'] = melted_correlations.apply(lambda x: (x["Target"], x["Input"]), axis = 1)
factors = list(melted_correlations.cats)[::-1]
p = bokeh.plotting.figure(
height=340,
width=440,
x_axis_label="Spearman Correlation",
x_range=[0, 1],
y_range=bokeh.models.FactorRange(*factors),
tools="save",
title = title
)
p.hbar(
source=melted_correlations,
y="cats",
right="Correlation",
height=0.6,
line_color = 'black',
fill_color=bokeh.palettes.Dark2_6[0]
)
labels_df = melted_correlations
labels_df['corr_str'] = labels_df['Correlation'].apply(lambda x: round(x, 2)).astype(str)
labels_source = bokeh.models.ColumnDataSource(labels_df)
labels = bokeh.models.LabelSet(x='Correlation', y='cats', text='corr_str',text_font_size = "10px",
x_offset=12, y_offset=-5, source=labels_source, render_mode='canvas')
p.ygrid.grid_line_color = None
p.y_range.range_padding = 0.1
p.add_layout(labels)
p.legend.visible = False
p.output_backend = "svg"
return p
if __name__ == '__main__':
datasets = [cr9114_dict, cr6261_dict, (g6HC_dict, g6LC_dict) ]
all_corr_plots = []
for d in datasets:
if type(d) is tuple:
g6Hc, g6Lc = d
title = g6Hc['ab_name'] + ', ' + g6Hc['expt_type']
g6_combined = get_g6_corr(g6Hc, g6Lc)
all_corr_plots.append(plot_hbar(title, g6_combined, g6Lc['palette']))
else:
corr_df = get_corr(d['ab_name'], d['files'], d['dms_df'], d['ag_columns'])
title = d['ab_name'] + ', ' + d['expt_type']
all_corr_plots.append(plot_hbar(title, corr_df, d['palette']))
all_corr_fname = f"output/ab_mutagenesis_expts/mpnn_benchmarks.html"
bokeh.plotting.output_file(all_corr_fname)
bokeh.io.show(bokeh.layouts.gridplot(all_corr_plots, ncols = len(all_corr_plots)))
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