a0f64fd365
and old firing_characterization file to firing_characterization_old.py also renamed ramp_examples.py as ramp_firing.py; cleaned up comments and code for rheobase_correlation.py, AUC_correlation.py, diversity_in_firing.py, ramp_firing.py, simulation_model_comparison.py
246 lines
11 KiB
Python
246 lines
11 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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import os
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import string
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from Figures.plotstyle import sim_style
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import seaborn as sns
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import scipy.stats as stats
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import matplotlib.lines as mlines
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def cm2inch(*tupl):
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inch = 2.54
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if isinstance(tupl[0], tuple):
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return tuple(i/inch for i in tupl[0])
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else:
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return tuple(i/inch for i in tupl)
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def Kendall_tau(df):
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tau = df.corr(method='kendall')
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p = pd.DataFrame(columns=df.columns, index=df.columns)
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for col in range((df.columns).shape[0]):
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for col2 in range((df.columns).shape[0]):
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if col != col2:
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_, p.loc[df.columns[col], df.columns[col2]] = stats.kendalltau(
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df[df.columns[col]], df[df.columns[col2]], nan_policy='omit')
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return tau, p
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def correlation_plot(ax, df='AUC', title='', cbar=False):
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cbar_ax = fig.add_axes([0.685, 0.44, .15, .01])
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cbar_ax.spines['left'].set_visible(False)
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cbar_ax.spines['bottom'].set_visible(False)
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cbar_ax.spines['right'].set_visible(False)
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cbar_ax.spines['top'].set_visible(False)
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cbar_ax.set_xticks([])
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cbar_ax.set_yticks([])
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if df == 'AUC':
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df = pd.read_csv(os.path.join('./Figures/Data/sim_mut_AUC.csv'), index_col='Unnamed: 0')
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elif df == 'rheo':
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df = pd.read_csv(os.path.join('./Figures/Data/sim_mut_rheo.csv'), index_col='Unnamed: 0')
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# array for names
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cmap = sns.diverging_palette(220, 10, as_cmap=True)
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models = ['RS_pyramidal', 'RS_inhib', 'FS', 'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN',
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'STN_Kv', 'STN_Kv_only']
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model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'Cb stellate',
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'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
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'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN',
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'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
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col_dict = {}
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for m in range(len(models)):
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col_dict[models[m]] = model_names[m]
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df.rename(columns=col_dict, inplace=True)
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df = df[model_names]
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# calculate correlation matrix
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tau, p = Kendall_tau(df)
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tau = tau.drop(columns='STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', index='RS pyramidal')
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# mask to hide upper triangle of matrix
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mask = np.zeros_like(tau, dtype=bool)
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mask[np.triu_indices_from(mask)] = True
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np.fill_diagonal(mask, False)
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# models and renaming of tau
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models = ['RS pyramidal', 'RS inhibitory', 'FS', 'Cb stellate',
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'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
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'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN',
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'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
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model_names = ['RS pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
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'RS inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
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'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
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'Cb stellate', 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
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'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN',
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'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
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col_dict = {}
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for m in range(len(models)):
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col_dict[models[m]] = model_names[m]
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tau.rename(columns=col_dict, index=col_dict, inplace=True)
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tau = tau[model_names]
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# plotting with or without colorbar
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if cbar==False:
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res = sns.heatmap(tau, annot=False, mask=mask, center=0, vmax=1, vmin=-1, linewidths=.5, square=True, ax=ax,
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cbar=False, cmap=cmap, cbar_ax=cbar_ax, cbar_kws={"shrink": .52})
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else:
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res = sns.heatmap(tau, annot=False, mask=mask, center=0, vmax=1, vmin=-1, linewidths=.5, square=True, ax=ax,
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cbar=True, cmap=cmap, cbar_ax=cbar_ax,
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cbar_kws={"orientation": "horizontal",
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"ticks": [-1,-0.5, 0, 0.5, 1]} )
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cbar_ax.set_title(r'Kendall $\tau$', y=1.02, loc='center', fontsize=6)
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cbar_ax.tick_params(length=3)
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for tick in cbar_ax.xaxis.get_major_ticks():
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tick.label.set_fontsize(6)
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ax.set_title(title, fontsize=8)
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def mutation_plot(ax, model='RS_pramidal'):
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models = ['RS_pyramidal', 'RS_inhib', 'FS', 'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN',
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'STN_Kv', 'STN_Kv_only']
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model_names = ['RS pyramidal', 'RS inhibitory', 'FS',
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'Cb stellate', 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
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'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN', 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
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model_display_names = ['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'Cb stellate',
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'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
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'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN',
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'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
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col_dict = {}
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for m in range(len(models)):
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col_dict[models[m]] = model_display_names[m]
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ax_dict = {}
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ax_dict['RS_pyramidal'] = (0, 0)
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ax_dict['RS_inhib'] = (0, 1)
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ax_dict['FS'] = (1, 0)
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ax_dict['Cb_stellate'] = (2, 0)
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ax_dict['Cb_stellate_Kv'] = (2, 1)
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ax_dict['Cb_stellate_Kv_only'] = (3, 0)
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ax_dict['STN'] = (3, 1)
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ax_dict['STN_Kv'] = (4, 0)
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ax_dict['STN_Kv_only'] = (4, 1)
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ylim_dict = {}
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ylim_dict['RS_pyramidal'] = (-0.1, 0.3)
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ylim_dict['RS_inhib'] = (-0.6, 0.6)
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ylim_dict['FS'] = (-0.06, 0.08)
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ylim_dict['Cb_stellate'] = (-0.1, 0.4)
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ylim_dict['Cb_stellate_Kv'] = (-0.1, 0.5)
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ylim_dict['Cb_stellate_Kv_only'] = (-1, 0.8)
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ylim_dict['STN'] = (-0.01, 0.015)
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ylim_dict['STN_Kv'] = (-0.4, 0.6)
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ylim_dict['STN_Kv_only'] = (-0.03, 0.3)
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Marker_dict = {'Cb stellate': 'o', 'RS Inhibitory': 'o', 'FS': 'o', 'RS Pyramidal': "^",
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'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "^",
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'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "^",
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'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "D",
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'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "D",
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'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "D",
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'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "s",
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'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "s", 'STN': "s"}
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AUC = pd.read_csv(os.path.join('./Figures/Data/sim_mut_AUC.csv'), index_col='Unnamed: 0')
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rheo = pd.read_csv(os.path.join('./Figures/Data/sim_mut_rheo.csv'), index_col='Unnamed: 0')
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mod = models.index(model)
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mut_names = AUC.index
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ax.plot(rheo.loc[mut_names, model_names[mod]]*1000, AUC.loc[mut_names, model_names[mod]], linestyle='',
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markeredgecolor='grey', markerfacecolor='grey', marker=Marker_dict[model_display_names[mod]],
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markersize=2)
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ax.plot(rheo.loc['wt', model_names[mod]], AUC.loc['wt', model_names[mod]], 'sk')
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mut_col = sns.color_palette("pastel")
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ax.plot(rheo.loc['V174F', model_names[mod]]*1000, AUC.loc['V174F', model_names[mod]], linestyle='',
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markeredgecolor=mut_col[0], markerfacecolor=mut_col[0], marker=Marker_dict[model_display_names[mod]],markersize=4)
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ax.plot(rheo.loc['F414C', model_names[mod]]*1000, AUC.loc['F414C', model_names[mod]], linestyle='',
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markeredgecolor=mut_col[1], markerfacecolor=mut_col[1], marker=Marker_dict[model_display_names[mod]],markersize=4)
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ax.plot(rheo.loc['E283K', model_names[mod]]*1000, AUC.loc['E283K', model_names[mod]], linestyle='',
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markeredgecolor=mut_col[2], markerfacecolor=mut_col[2], marker=Marker_dict[model_display_names[mod]],markersize=4)
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ax.plot(rheo.loc['V404I', model_names[mod]]*1000, AUC.loc['V404I', model_names[mod]], linestyle='',
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markeredgecolor=mut_col[3], markerfacecolor=mut_col[5], marker=Marker_dict[model_display_names[mod]],markersize=4)
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ax.set_title(model_display_names[mod], pad=14)
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ax.set_xlabel('$\Delta$ Rheobase (pA)')
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ax.set_ylabel('Normalized $\Delta$AUC')
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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ax.ticklabel_format(axis="y", style="sci", scilimits=(0, 0),useMathText=True)
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xmin, xmax = ax.get_xlim()
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ymin, ymax = ax.get_ylim()
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ax.hlines(0, xmin, xmax, colors='lightgrey', linestyles='--')
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ax.vlines(0, ymin,ymax, colors='lightgrey', linestyles='--')
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return ax
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def mutation_legend(ax, marker_s_leg, pos, ncol):
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colors = sns.color_palette("pastel")
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Markers = ["o", "o", "o", "o"]
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V174F = mlines.Line2D([], [], color=colors[0], marker=Markers[0], markersize=marker_s_leg, linestyle='None',
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label='V174F')
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F414C = mlines.Line2D([], [], color=colors[1], marker=Markers[1], markersize=marker_s_leg, linestyle='None',
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label='F414C')
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E283K = mlines.Line2D([], [], color=colors[2], marker=Markers[2], markersize=marker_s_leg, linestyle='None', label='E283K')
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V404I = mlines.Line2D([], [], color=colors[5], marker=Markers[3], markersize=marker_s_leg, linestyle='None',
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label='V404I')
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WT = mlines.Line2D([], [], color='k', marker='s', markersize=marker_s_leg+2, linestyle='None', label='Wild type')
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ax.legend(handles=[WT, V174F, F414C, E283K, V404I], loc='center', bbox_to_anchor=pos, ncol=ncol, frameon=False)
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sim_style()
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# plot setup
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fig = plt.figure()
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gs0 = fig.add_gridspec(1, 6, wspace=-0.2)
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gsl = gs0[0:3].subgridspec(3, 3, wspace=0.9, hspace=0.8)
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gsr = gs0[4:6].subgridspec(6, 1, wspace=0.6, hspace=1)
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ax00 = fig.add_subplot(gsl[0,0])
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ax01 = fig.add_subplot(gsl[0,1])
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ax02 = fig.add_subplot(gsl[0,2])
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ax10 = fig.add_subplot(gsl[1,0])
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ax11 = fig.add_subplot(gsl[1,1])
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ax12 = fig.add_subplot(gsl[1,2])
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ax20 = fig.add_subplot(gsl[2,0])
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ax21 = fig.add_subplot(gsl[2,1])
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ax22 = fig.add_subplot(gsl[2,2])
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axr0 = fig.add_subplot(gsr[0:2,0])
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axr1 = fig.add_subplot(gsr[4:,0])
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# plot mutations in each model
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ax00 = mutation_plot(ax00, model='RS_pyramidal')
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ax01 = mutation_plot(ax01, model='RS_inhib')
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ax02 = mutation_plot(ax02, model='FS')
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ax10 = mutation_plot(ax10, model='Cb_stellate')
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ax11 = mutation_plot(ax11, model='Cb_stellate_Kv')
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ax12 = mutation_plot(ax12, model='Cb_stellate_Kv_only')
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ax20 = mutation_plot(ax20, model='STN')
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ax21 = mutation_plot(ax21, model='STN_Kv')
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ax22 = mutation_plot(ax22, model='STN_Kv_only')
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marker_s_leg = 4
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pos = (0.425, -0.7)
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ncol = 5
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mutation_legend(ax21, marker_s_leg, pos, ncol)
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# plot correlation matrices
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correlation_plot(axr1,df = 'AUC', title='Normalized $\Delta$AUC', cbar=False)
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correlation_plot(axr0,df = 'rheo', title='$\Delta$ Rheobase', cbar=True)
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# add subplot labels
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axs = [ax00, ax01,ax02, ax10, ax11, ax12, ax20, ax21, ax22]
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j=0
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for i in range(0,9):
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axs[i].text(-0.48, 1.175, string.ascii_uppercase[i], transform=axs[i].transAxes, size=10, weight='bold')
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j +=1
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axr0.text(-0.77, 1.1, string.ascii_uppercase[j], transform=axr0.transAxes, size=10, weight='bold')
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axr1.text(-0.77, 1.1, string.ascii_uppercase[j+1], transform=axr1.transAxes, size=10, weight='bold')
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# save
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fig.set_size_inches(cm2inch(22.2,15))
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fig.savefig('./Figures/simulation_model_comparison.pdf', dpi=fig.dpi)
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plt.show()
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