import numpy as np 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 hv.extension("bokeh") import warnings # Suppress FutureWarning messages warnings.simplefilter(action='ignore',) # Your Pandas code here if_conds = ['ag+ab', 'ab only', 'ab'] cr9114_dict = { 'ab_name' : 'CR9114', 'files' : { 'Ab-Ag': 'output/ab_mutagenesis_expts/cr9114/4fqi_ablh_scores.csv', 'Ab only': 'output/ab_mutagenesis_expts/cr9114/4fqi_lh_scores.csv', 'Ab VH only': 'output/ab_mutagenesis_expts/cr9114/4fqi_h_scores.csv', 'ESM-1v': 'output/ab_mutagenesis_expts/cr9114/cr9114_exp_data_maskMargLabeled.csv', 'AbLang': 'output/ab_mutagenesis_expts/cr9114/cr9114_hc_ablangScores.csv', 'abYsis': 'output/ab_mutagenesis_expts/cr9114/abysis_counts_cr9114_vh.txt', }, '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' : { 'Ab-Ag': 'output/ab_mutagenesis_expts/cr6261/3gbn_ablh_scores.csv', 'Ab only': 'output/ab_mutagenesis_expts/cr6261/3gbn_lh_scores.csv', 'Ab VH only': 'output/ab_mutagenesis_expts/cr6261/3gbn_h_scores.csv', 'ESM-1v': 'output/ab_mutagenesis_expts/cr6261/cr6261_exp_data_maskMargLabeled.csv', 'AbLang': 'output/ab_mutagenesis_expts/cr6261/cr6261_hc_ablangScores.csv', 'abYsis': 'output/ab_mutagenesis_expts/cr6261/abysis_counts_cr6261_vh.txt', }, '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' : { 'Ab-Ag': 'output/ab_mutagenesis_expts/g6/2fjg_vlh_lc_scores.csv', 'Ab only': 'output/ab_mutagenesis_expts/g6/2fjg_lh_lc_scores.csv', 'Ab VH/VL only': 'output/ab_mutagenesis_expts/g6/2fjg_l_lc_scores.csv', 'ESM-1v': 'output/ab_mutagenesis_expts/g6/g6Lc_exp_data_maskMargLabeled.csv', 'AbLang': 'output/ab_mutagenesis_expts/g6/g6_lc_ablangScores.csv', 'abYsis': 'output/ab_mutagenesis_expts/g6/abysis_counts_g6_vl.txt', }, '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' : { 'Ab-Ag': 'output/ab_mutagenesis_expts/g6/2fjg_vlh_hc_scores.csv', 'Ab only': 'output/ab_mutagenesis_expts/g6/2fjg_lh_hc_scores.csv', 'Ab VH/VL only': 'output/ab_mutagenesis_expts/g6/2fjg_h_hc_scores.csv', 'ESM-1v': 'output/ab_mutagenesis_expts/g6/g6Hc_exp_data_maskMargLabeled.csv', 'AbLang': 'output/ab_mutagenesis_expts/g6/g6_hc_ablangScores.csv', 'abYsis': 'output/ab_mutagenesis_expts/g6/abysis_counts_g6_vh.txt', }, '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 apply_mask_and_average(scores, genotype): #scores here should already be a in log space if '1' in genotype: masked_list = [] for s, mask_char in zip(scores, genotype): if mask_char == '1': masked_list.append(s) multi_avg = np.mean(masked_list) else: #if genotype is all zeros = wt multi_avg = 0 return multi_avg def transform_single_to_multi( dms_df, singleMuts_df, condition, sort_col, ascending ): multi_scores = [] #use input parameter as method bc key is kwarg in pd.sort_values #method should be either esm1v or abysis or AbLang #for cr sorts the single mutation dataframe in residue order, ie binary '10000' before '00010'. ascending = False #for g6 sorts the single mutation dataframe in position order, ie residue index. ascending = True singleMuts_sorted = singleMuts_df.sort_values(sort_col, key=lambda x: x.astype(int), ascending=ascending) singles_scores = singleMuts_sorted[condition].to_list() for g in dms_df['genotype']: multi_scores.append(apply_mask_and_average(singles_scores, g )) return multi_scores def get_abysis_single_scores_df(singleMut_ids, abysis_df): single_scores = [] all_pos = [] for id in singleMut_ids: wt, pos, mt = id[0], int(id[1:-1]), id[-1] likelihood_ratio = abysis_df.loc[(abysis_df['pos'] == pos) & (abysis_df['wt'] == wt) & (abysis_df['mt'] == mt)]['likelihood_ratio'].to_list()[0] log_like_ratio = np.log10(likelihood_ratio) single_scores.append(log_like_ratio) all_pos.append(pos) singleMuts_df = pd.DataFrame({'pos': all_pos, 'abYsis': single_scores}) return singleMuts_df def get_ablang_single_scores_df(singleMut_ids, ablang_df): single_scores = [] all_pos = [] for id in singleMut_ids: wt, pos, mt = id[0], int(id[1:-1]), id[-1] log_likelihood_ratio = ablang_df.loc[(ablang_df['pos'] == pos) & (ablang_df['wt'] == wt) & (ablang_df['mt'] == mt)]['log_likelihood_ratio'].to_list()[0] single_scores.append(log_likelihood_ratio) all_pos.append(pos) singleMuts_df = pd.DataFrame({'pos': all_pos, 'AbLang': single_scores}) return singleMuts_df #get melted correlations matrix of structure input x target antigen, for a given antibody def get_corr(ab_name, files, dms_df, ag_columns, dropLOQ = False ): inv_fold_files = {key: value for key, value in files.items() if key not in ['ESM-1v', 'AbLang', 'abYsis']} other_files = {key: value for key, value in files.items() if key in ['ESM-1v', 'AbLang', 'abYsis']} #retreive correlations from inverse fold scores for key, filepath in inv_fold_files.items(): df = pd.read_csv(filepath) # Read the CSV file into a DataFrame column_label = key log_likelihood_column = df['log_likelihood'] # Extract the 'log_likelihood' column dms_df[column_label] = log_likelihood_column #retreive correlations from abysis and esm1v scores for key, filepath in other_files.items(): if key == 'ESM-1v': singleMuts_df = pd.read_csv(filepath) # Get the columns for all esm1v models esm_columns = [col for col in singleMuts_df.columns if col.startswith('esm')] # Calculate the average for each esm column esm_avg_values = singleMuts_df[esm_columns].mean(axis=1) singleMuts_df[key] = esm_avg_values if ab_name == 'g6': #g6 data set is only single mutations dms_df[key] = esm_avg_values else: #cr datasets are multiple mutations dms_df[key] = transform_single_to_multi(dms_df, singleMuts_df, key, 'genotype', ascending = False) elif key == 'abYsis': abysis_df = pd.read_csv(filepath, sep = '\t', dtype = {'pos': int}) if ab_name == 'g6': dms_df[key] = get_abysis_single_scores_df(dms_df['mutant'], abysis_df)[key] else: #cr data set are multiple mutations. use same scoring strategy as esm1v paper singleMut_ids = pd.read_csv(files['ESM-1v'])['mutant'].to_list() singleMuts_df = get_abysis_single_scores_df(singleMut_ids, abysis_df) dms_df[key] = transform_single_to_multi(dms_df, singleMuts_df, key, 'pos', ascending = True) elif key == 'AbLang': ablang_df = pd.read_csv(filepath, dtype = {'pos': int}) if ab_name == 'g6': dms_df[key] = get_ablang_single_scores_df(dms_df['mutant'], ablang_df)[key] else: #cr data set are multiple mutations. use same scoring strategy as esm1v paper singleMut_ids = pd.read_csv(files['ESM-1v'])['mutant'].to_list() singleMuts_df = get_ablang_single_scores_df(singleMut_ids, ablang_df) dms_df[key] = transform_single_to_multi(dms_df, singleMuts_df, key, 'pos', ascending = True) conditions = list(files.keys()) # if not dropLOQ: 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'}) ) melted_correlations['Method'] = ['LM' if (('ESM' in i) or ('AbLang' in i)) else 'IF' if 'Ab' in i else 'MSA' for i in melted_correlations['Input'] ] return melted_correlations #if we wish to drop the samples on the lower limit of quantitation, drop samples from each ag group individually and compute correlation with IF log_like else: all_ag_melted = pd.DataFrame({}) for ag in ag_columns: filt_df = dms_df[dms_df[ag] > min(dms_df[ag])] correlations = filt_df[[ag] + conditions].corr(method='spearman') correlations = correlations.drop(conditions, axis= 1) correlations = correlations.drop(ag, 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'}) all_ag_melted = pd.concat([all_ag_melted, melted_correlations], ignore_index= True) all_ag_melted['Method'] = ['LM' if (('ESM' in i) or ('AbLang' in i)) else 'IF' if 'Ab' in i else 'MSA' for i in all_ag_melted['Input'] ] return all_ag_melted #get melted correlations matrix of structure input x target antigen, for a given 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'] inv_fold_files = {key: value for key, value in files.items() if key not in ['ESM-1v', 'AbLang', 'abYsis']} other_files = {key: value for key, value in files.items() if key in ['ESM-1v', 'AbLang', 'abYsis']} #retreive correlations from inverse fold scores for key, filepath in inv_fold_files.items(): df = pd.read_csv(filepath) # Read the CSV file into a DataFrame column_label = key log_likelihood_column = df['log_likelihood'] # Extract the 'log_likelihood' column dms_df[column_label] = log_likelihood_column #retreive correlations from abysis, ablang, and esm1v scores for key, filepath in other_files.items(): if key == 'ESM-1v': singleMuts_df = pd.read_csv(filepath) # Get the columns for all esm1v models esm_columns = [col for col in singleMuts_df.columns if col.startswith('esm')] # Calculate the average for each esm column esm_avg_values = singleMuts_df[esm_columns].mean(axis=1) singleMuts_df[key] = esm_avg_values if ab_name == 'g6': #g6 data set is only single mutations dms_df[key] = esm_avg_values elif key == 'abYsis': abysis_df = pd.read_csv(filepath, sep = '\t', dtype = {'pos': int}) if ab_name == 'g6': dms_df[key] = get_abysis_single_scores_df(dms_df['mutant'], abysis_df)[key] elif key == 'AbLang': ablang_df = pd.read_csv(filepath, dtype = {'pos': int}) if ab_name == 'g6': dms_df[key] = get_ablang_single_scores_df(dms_df['mutant'], ablang_df)[key] vh_and_vl_dms_df = pd.concat([vh_and_vl_dms_df, dms_df], ignore_index= True) conditions = list(files.keys()) # if not dropLOQ: 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['Method'] = ['LM' if (('ESM' in i) or ('AbLang' in i)) else 'IF' if 'Ab' in i else 'MSA' for i in melted_correlations['Input'] ] melted_correlations['Target'] = ['VEGF-A'] * len(melted_correlations) return melted_correlations #if we wish to drop the samples on the lower limit of quantitation, drop samples from each ag group individually and compute correlation with IF log_like else: all_ag_melted = pd.DataFrame({}) for ag in ag_columns: filt_df = vh_and_vl_dms_df[vh_and_vl_dms_df[ag] > min(vh_and_vl_dms_df[ag])] correlations = filt_df[[ag] + conditions].corr(method='spearman') correlations = correlations.drop(conditions, axis= 1) correlations = correlations.drop(ag, 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'}) all_ag_melted = pd.concat([all_ag_melted, melted_correlations], ignore_index= True) all_ag_melted['Method'] = ['LM' if (('ESM' in i) or ('AbLang' in i)) else 'IF' if 'Ab' in i else 'MSA' for i in all_ag_melted['Input'] ] melted_correlations['Target'] = ['VEGF-A'] * len(melted_correlations) return all_ag_melted #plot correlation bars for cr antibodies: cr6261 and cr9114 def plot_hbar(title, melted_correlations, palette = bokeh.palettes.Spectral6, compare = 'model'): if compare == 'model': melted_correlations.replace('Ab-Ag','InverseFolding', inplace = True) color_by = 'Input' elif compare == 'IF': color_by = 'Method' else: color_by = 'Target' melted_correlations['cats'] = melted_correlations.apply(lambda x: (x["Target"], x["Input"]), axis = 1) factors = list(melted_correlations.cats)[::-1] if 'CR6261' in title: x_min = -0.15 else: x_min = 0 p = bokeh.plotting.figure( height=340, width=440, x_axis_label="Spearman Correlation", x_range=[x_min, 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', legend_field = color_by, # use the palette to colormap based on the the x[1:2] values fill_color=factor_cmap( color_by, palette= palette, factors = list(melted_correlations[color_by].unique()), start=1, end=3) ) #labels_df = melted_correlations[melted_correlations['Input'].isin(['Ab-Ag','ESM-1v', 'abYsis', 'InverseFold'])] 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) if compare == 'IF': p.legend.orientation = "vertical" p.legend.location = "bottom_right" p.legend.label_text_font_size = "8pt" else: p.legend.visible = False p.output_backend = "svg" return p def plot_g6_scatter(g6Hc_df, g6Lc_df, ag_columns, dropLOQ): ag = ag_columns[0] if dropLOQ: g6Lc_df = g6Lc_df[g6Lc_df[ag] > np.min(g6Lc_df[ag])] g6Hc_df = g6Hc_df[g6Hc_df[ag] > np.min(g6Hc_df[ag])] hv.extension("bokeh") plots = [] for (plot_df,title) in [(g6Hc_df, 'g6.31 VH'),(g6Lc_df, 'g6.31 VL')]: # Generate HoloViews Points Element points = hv.Points( data=plot_df, kdims=[ag, 'Ab-Ag'], ) # Datashade with spreading of points p = hv.operation.datashader.dynspread( hv.operation.datashader.rasterize( points ).opts( cmap='Magma', cnorm='linear', ) ).opts( frame_width=350, frame_height=300, padding=0.05, show_grid=False, colorbar = True ) p = hv.render(p) p.output_backend = "svg" plots.append(p) return plots #plot scatter plot colored by n_muts for CR antibodies def single_cr_scatterMut(plot_df, ag_columns, x, y, title, ): x_axis_label = x + '-logKd' if x in ag_columns else 'log likelihood' y_axis_label = y + '-logKd' if y in ag_columns else 'log likelihood' #set up figure p = bokeh.plotting.figure( width=500, height=400, x_axis_label= x_axis_label, y_axis_label= y_axis_label, title = title ) #Set up color mapper to color by number of mutations from germline mapper = bokeh.transform.linear_cmap(field_name='som_mut', palette=bokeh.palettes.inferno(16) ,low=min(plot_df.som_mut) ,high=max(plot_df.som_mut)) p.circle( source=plot_df, x= x, y= y, alpha = 0.2, line_color=mapper, color=mapper, size = 4 ) # p.diamond( #germline seqid is all 0's source = plot_df[plot_df['genotype'].str.contains('1') == False], x= x, y= y, color = 'dodgerblue', size = 12, legend_label = 'germline' ) p.star( #mature seqid is all 0's source = plot_df[plot_df['genotype'].str.contains('0') == False], x= x, y= y, color = 'green', size = 12, legend_label = 'mature' ) p.legend.location = "top_left" p.output_backend = "svg" return p, mapper #plot single scatter plot for CR antibodies - with dynamic spreading and datashading def single_cr_scatter(plot_df, ag_columns, x, y, title, ): x_axis_label = x + '-logKd' if x in ag_columns else 'log likelihood' y_axis_label = y + '-logKd' if y in ag_columns else 'log likelihood' hv.extension("bokeh") # Generate HoloViews Points Element points = hv.Points( data=plot_df, kdims=[x, y], ) # Datashade with spreading of points p = hv.operation.datashader.dynspread( hv.operation.datashader.rasterize( points ).opts( cmap='Magma', cnorm='linear', ) ).opts( frame_width=350, frame_height=300, padding=0.05, show_grid=False, colorbar = True ) p = hv.render(p) p.grid.visible = False # p.diamond( #germline seqid is all 0's source = plot_df[plot_df['genotype'].str.contains('1') == False], x= x, y= y, color = 'dodgerblue', size = 12, #legend_label = 'germline' ) p.star( #mature seqid is all 0's source = plot_df[plot_df['genotype'].str.contains('0') == False], x= x, y= y, color = 'green', size = 12, #legend_label = 'mature' ) #p.legend.location = "top_left" p.output_backend = "svg" return p def plot_CR_scatter_colorMuts(title, files, dms_df, ag_columns, dropLOQ = False): agAb_files = {key: value for key, value in files.items() if key in ['Ab-Ag',]} for key, filepath in agAb_files.items(): df = pd.read_csv(filepath) # Read the CSV file into a DataFrame column_label = key log_likelihood_column = df['log_likelihood'] # Extract the 'log_likelihood' column dms_df[column_label] = log_likelihood_column plots = [] #predictions vs experiment for ag in ag_columns: if dropLOQ: plot_df = dms_df[dms_df[ag] > min(dms_df[ag])] #add back wt which is on LOQ for visualization plot_df = pd.concat([plot_df, dms_df[dms_df['som_mut'] == 0]], ignore_index = True, axis = 0) else: plot_df = dms_df p, _ = single_cr_scatterMut(plot_df, ag_columns, x = ag, y = 'Ab-Ag', title = f'{title} against {ag}') plots.append(p) #experimental vs experimental plot_df = dms_df if dropLOQ: for ag in ag_columns: plot_df = plot_df[plot_df[ag] > min(plot_df[ag])] #add back wt which is on LOQ for visualization plot_df = pd.concat([plot_df, dms_df[dms_df['som_mut'] == 0]], ignore_index = True, axis = 0) p_exp, mapper = single_cr_scatterMut(plot_df, ag_columns, x= ag_columns[0], y = ag_columns[1], title = 'Experimental Cross-Reactive Binding Landscape') p_exp.legend.location = "top_left" color_bar = bokeh.models.ColorBar(color_mapper=mapper['transform'], width=8, title = 'Amino Acid Mutations') #p_exp.add_layout(color_bar, 'right') plots.append(p_exp) p.output_backend = "svg" return plots def plot_CR_scatterplots(title, files, dms_df, ag_columns, dropLOQ = False): agAb_files = {key: value for key, value in files.items() if key in ['Ab-Ag',]} for key, filepath in agAb_files.items(): df = pd.read_csv(filepath) # Read the CSV file into a DataFrame column_label = key log_likelihood_column = df['log_likelihood'] # Extract the 'log_likelihood' column dms_df[column_label] = log_likelihood_column plots = [] #predictions vs experiment for ag in ag_columns: if dropLOQ: plot_df = dms_df[dms_df[ag] > min(dms_df[ag])] #add back wt which is on LOQ for visualization plot_df = pd.concat([plot_df, dms_df[dms_df['som_mut'] == 0]], ignore_index = True, axis = 0) else: plot_df = dms_df p = single_cr_scatter(plot_df, ag_columns, x = ag, y = 'Ab-Ag', title = f'{title} against {ag}') plots.append(p) #experimental vs experimental plot_df = dms_df if dropLOQ: for ag in ag_columns: plot_df = plot_df[plot_df[ag] > min(plot_df[ag])] #add back wt which is on LOQ for visualization plot_df = pd.concat([plot_df, dms_df[dms_df['som_mut'] == 0]], ignore_index = True, axis = 0) p_exp = single_cr_scatter(plot_df, ag_columns, x= ag_columns[0], y = ag_columns[1], title = 'Experimental Cross-Reactive Binding Landscape') plots.append(p_exp) p_exp.output_backend = "svg" return plots #plot correlation bars for cr antibodies: cr6261 and cr9114 def plot_LOQ_comparison(ab_name, unfilt_corrs, filt_corrs, palette = bokeh.palettes.Spectral6,): unfilt_corrs['LOQ'] = ['Included'] * len(unfilt_corrs) filt_corrs['LOQ'] = ['Excluded'] * len(filt_corrs) combined_melt = pd.concat([unfilt_corrs,filt_corrs], ignore_index= True) combined_melt['cats'] = combined_melt.apply(lambda x: (x["Target"], x["Input"], x["LOQ"]), axis = 1) factors = list(combined_melt.cats)[::-1] p = bokeh.plotting.figure( height=850, width=400, x_axis_label="Spearman Correlation", x_range=[-.25, 1], y_range=bokeh.models.FactorRange(*factors), tools="save", title = f'{ab_name}, Impact of Ignoring Lower LOQ ' ) p.hbar( source=combined_melt, y="cats", right="Correlation", height=0.7, line_color = 'black', legend_field = 'LOQ', # use the palette to colormap based on the the x[1:2] values fill_color=factor_cmap( 'LOQ', palette= palette, factors = list(combined_melt['LOQ'].unique()), start=1, end=2) ) p.ygrid.grid_line_color = None p.y_range.range_padding = 0.1 p.legend.location = "bottom_right" p.legend.orientation = "vertical" p.yaxis.axis_label = "Model Input" p.output_backend = "svg" return p if __name__ == '__main__': datasets = [cr9114_dict, cr6261_dict, (g6HC_dict, g6LC_dict) ] results = [] #compute with and without points on lower limit of quantitation ignored for dropLOQ in [False, True]: g6_corrs = [] 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, dropLOQ= dropLOQ) all_corr_plots.append(plot_hbar(title, g6_combined, g6Lc['palette'], 'IF')) #also plot scatter plots scatter_fname = f"output/ab_mutagenesis_expts/g6_scatterplot_colorMuts{'_dropLOQ' if dropLOQ else ''}.html" bokeh.plotting.output_file(scatter_fname) g6_scatterMuts_plots = plot_g6_scatter(g6Hc['dms_df'], g6Lc['dms_df'], g6Hc['ag_columns'], dropLOQ= dropLOQ) bokeh.io.show(bokeh.layouts.gridplot(g6_scatterMuts_plots, ncols = 2)) if (dropLOQ == False): results.append(g6_combined) else: corr_df = get_corr(d['ab_name'], d['files'], d['dms_df'], d['ag_columns'], dropLOQ= dropLOQ) title = d['ab_name'] + ', ' + d['expt_type'] all_corr_plots.append(plot_hbar(title, corr_df, d['palette'], 'IF')) #also plot scatter plots for CR9114, CR6261 scatter_fname = f"output/ab_mutagenesis_expts/{d['ab_name']}_scatter_colorMuts{'_dropLOQ' if dropLOQ else ''}.html" bokeh.plotting.output_file(scatter_fname) cr_scatterMuts_plots = plot_CR_scatter_colorMuts(title, d['files'], d['dms_df'], d['ag_columns'], dropLOQ= dropLOQ) bokeh.io.show(bokeh.layouts.gridplot(cr_scatterMuts_plots, ncols = 3)) #also scatter plots with datashading/dynamic spreading for CR9114, CR6261 scatter_fname = f"output/ab_mutagenesis_expts/{d['ab_name']}_scatter{'_dropLOQ' if dropLOQ else ''}.html" bokeh.plotting.output_file(scatter_fname) cr_scattter_plots = plot_CR_scatterplots(title, d['files'], d['dms_df'], d['ag_columns'], dropLOQ= dropLOQ) bokeh.io.show(bokeh.layouts.gridplot(cr_scattter_plots, ncols = 3)) if (dropLOQ == False): results.append(corr_df) all_corr_fname = f"output/ab_mutagenesis_expts/benchmarks{'_dropLOQ' if dropLOQ else ''}.html" bokeh.plotting.output_file(all_corr_fname) bokeh.io.show(bokeh.layouts.gridplot(all_corr_plots, ncols = len(all_corr_plots))) compareLOQ_fname = 'output/ab_mutagenesis_expts/LOQ_comparison.html' bokeh.plotting.output_file(compareLOQ_fname) compareLOQ_plots = [] for d in datasets: if (type(d) != tuple) and (d['ab_name'].startswith('CR')): corr_withLOQ = get_corr(d['ab_name'], d['files'], d['dms_df'], d['ag_columns'], ) corr_withoutLOQ = get_corr(d['ab_name'], d['files'], d['dms_df'], d['ag_columns'], dropLOQ= dropLOQ) compareLOQ_plots.append(plot_LOQ_comparison(d['ab_name'], corr_withLOQ, corr_withoutLOQ , d['palette'])) bokeh.io.show(bokeh.layouts.gridplot(compareLOQ_plots, ncols = len(compareLOQ_plots))) pd.concat(results, ignore_index= True).to_csv( 'output/ab_mutagenesis_expts/results.csv' , index = False)