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added figure plotting scripts *_letters.py to plot using models named by letter

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nkoch1 2022-09-24 16:35:13 +02:00
parent 3ba75f8ce2
commit b005b04937
10 changed files with 1837 additions and 21 deletions
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Figures/AUC_correlation.pdf
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Figures/AUC_correlation.py
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@ -389,6 +389,21 @@ color_dict = {'Cb stellate': '#40A787', # cyan'#
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#873770', # magenta 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#873770', # magenta
'STN': '#D03050' # pink 'STN': '#D03050' # pink
} }
model_letter = {
'Cb stellate': 'A',
'RS Inhibitory': 'B',
'FS': 'C',
'RS Pyramidal': 'D',
'RS Inhibitory': 'E',
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'F',
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'G',
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'H',
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'I',
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'J',
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'K',
'STN':'L',
}
# plot setup # plot setup
marker_s_leg = 2 marker_s_leg = 2

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Figures/AUC_correlation_letters.py Normal file
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@ -0,0 +1,543 @@
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 3 19:52:04 2021
@author: nils
"""
import pandas as pd
import numpy as np
import string
import textwrap
import json
import matplotlib
import matplotlib.lines as mlines
from matplotlib import ticker
from matplotlib.ticker import NullFormatter
from Figures.plotstyle import boxplot_style
def cm2inch(*tupl):
inch = 2.54
if isinstance(tupl[0], tuple):
return tuple(i/inch for i in tupl[0])
else:
return tuple(i/inch for i in tupl)
#%% ##################### From https://stackoverflow.com/questions/52878845/swarmplot-with-hue-affecting-marker-beyond-color ##
# to change marker types in seaborn swarmplot
import seaborn as sns
import matplotlib.pyplot as plt
############## Begin hack ##############
from matplotlib.axes._axes import Axes
from matplotlib.markers import MarkerStyle
from numpy import ndarray
def GetColor2Marker(markers):
colorslist = ['#40A787', # cyan'#
'#F0D730', # yellow
'#C02717', # red
'#007030', # dark green
'#AAB71B', # lightgreen
'#008797', # light blue
'#F78017', # orange
'#478010', # green
'#53379B', # purple
'#2060A7', # blue
'#873770', # magenta
'#D03050' # pink
]
import matplotlib.colors
palette = [matplotlib.colors.to_rgb(c) for c in colorslist]
mkcolors = [(palette[i]) for i in range(len(markers))]
return dict(zip(mkcolors,markers))
def fixlegend(ax,markers,markersize=3,**kwargs):
# Fix Legend
legtitle = ax.get_legend().get_title().get_text()
_,l = ax.get_legend_handles_labels()
colorslist = ['#40A787', # cyan'#
'#F0D730', # yellow
'#C02717', # red
'#007030', # dark green
'#AAB71B', # lightgreen
'#008797', # light blue
'#F78017', # orange
'#478010', # green
'#53379B', # purple
'#2060A7', # blue
'#873770', # magenta
'#D03050' # pink
]
import matplotlib.colors
palette = [matplotlib.colors.to_rgb(c) for c in colorslist]
mkcolors = [(palette[i]) for i in range(len(markers))]
newHandles = [plt.Line2D([0],[0], ls="none", marker=m, color=c, mec="none", markersize=markersize,**kwargs) \
for m,c in zip(markers, mkcolors)]
ax.legend(newHandles,l)
leg = ax.get_legend()
leg.set_title(legtitle)
old_scatter = Axes.scatter
def new_scatter(self, *args, **kwargs):
colors = kwargs.get("c", None)
co2mk = kwargs.pop("co2mk",None)
FinalCollection = old_scatter(self, *args, **kwargs)
if co2mk is not None and isinstance(colors, ndarray):
Color2Marker = GetColor2Marker(co2mk)
paths=[]
for col in colors:
mk=Color2Marker[tuple(col)]
marker_obj = MarkerStyle(mk)
paths.append(marker_obj.get_path().transformed(marker_obj.get_transform()))
FinalCollection.set_paths(paths)
return FinalCollection
Axes.scatter = new_scatter
############## End hack. ##############
########################################################################################################################
#%% add gradient arrows
import matplotlib.pyplot as plt
import matplotlib.transforms
import matplotlib.path
from matplotlib.collections import LineCollection
def gradientaxis(ax, start, end, cmap, n=100,lw=1):
# Arrow shaft: LineCollection
x = np.linspace(start[0],end[0],n)
y = np.linspace(start[1],end[1],n)
points = np.array([x,y]).T.reshape(-1,1,2)
segments = np.concatenate([points[:-1],points[1:]], axis=1)
lc = LineCollection(segments, cmap=cmap, linewidth=lw,zorder=15)
lc.set_array(np.linspace(0,1,n))
ax.add_collection(lc)
return ax
#%%
#%%
def boxplot_with_markers(ax,max_width, alteration='shift', msize=3):
hlinewidth = 0.5
model_names = ['RS pyramidal','RS inhibitory','FS',
'RS pyramidal +$K_V1.1$','RS inhibitory +$K_V1.1$',
'FS +$K_V1.1$','Cb stellate','Cb stellate +$K_V1.1$',
'Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$',
'STN $\Delta$$K_V1.1$']
colorslist = ['#007030', # dark green
'#F0D730', # yellow
'#C02717', # red
'#478010', # green
'#AAB71B', # lightgreen
'#F78017', # orange
'#40A787', # cyan'#
'#008797', # light blue
'#2060A7', # blue
'#D03050', # pink
'#53379B', # purple
'#873770', # magenta
]
import matplotlib.colors
colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
clr_dict = {}
for m in range(len(model_names)):
clr_dict[model_names[m]] = colors[m]
print(colors)
print(clr_dict)
Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
if alteration=='shift':
i = 2 # Kd act
ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor = 'k')
df = pd.read_csv('./Figures/Data/AUC_shift_corr.csv')
sns.swarmplot(y="corr", x="$\Delta V_{1/2}$", hue="model", data=df,
palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
order=['Na activation', 'Na inactivation', 'K activation', '$K_V1.1$ activation',
'$K_V1.1$ inactivation', 'A activation', 'A inactivation'],
hue_order=model_names, co2mk=Markers)
lim = ax.get_xlim()
ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
ax.set_title("Shift ($\Delta V_{1/2}$)", y=1.05)
ax.set_xticklabels(['Na \nactivation', 'Na \ninactivation', 'K \nactivation', '$K_V1.1$ \nactivation',
'$K_V1.1$ \ninactivation', 'A \nactivation', 'A \ninactivation'])
elif alteration=='slope':
i = 3 # Kv1.1 act
ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor='k')
df = pd.read_csv('./Figures/Data/AUC_scale_corr.csv')
# Add in points to show each observation
sns.swarmplot(y="corr", x="Slope (k)", hue="model", data=df,
palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
order=['Na activation', 'Na inactivation', 'K activation', '$K_V1.1$ activation',
'$K_V1.1$ inactivation', 'A activation', 'A inactivation'],
hue_order=model_names, co2mk=Markers)
lim = ax.get_xlim()
ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
ax.set_title("Slope (k)", y=1.05)
ax.set_xticklabels(['Na \nactivation', 'Na \ninactivation', 'K \nactivation', '$K_V1.1$ \nactivation',
'$K_V1.1$ \ninactivation', 'A \nactivation', 'A \ninactivation'])
elif alteration=='g':
i = 1 # Kd
ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor='k')
df = pd.read_csv('./Figures/Data/AUC_g_corr.csv')
# Add in points to show each observation
sns.swarmplot(y="corr", x="g", hue="model", data=df,
palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
order=['Na', 'K', '$K_V1.1$', 'A', 'Leak'],
hue_order=model_names, co2mk=Markers)
lim = ax.get_xlim()
# ax.plot([lim[0], lim[1]], [0,0], ':k')
ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
# Tweak the visual presentation
ax.set_title("Conductance (g)", y=1.05)
ax.set_xticklabels(textwrap.fill(x.get_text(), max_width) for x in ax.get_xticklabels())
else:
print('Please chose "shift", "slope" or "g"')
ax.get_legend().remove()
ax.xaxis.grid(False)
sns.despine(trim=True, bottom=True, ax=ax)
ax.set(xlabel=None, ylabel=r'Kendall $\it{\tau}$')
def model_legend(ax, marker_s_leg, pos, ncol):
# colorslist = [ '#40A787', # cyan'#
# '#F0D730', # yellow
# '#C02717', # red
# '#007030', # dark green
# '#AAB71B', # lightgreen
# '#008797', # light blue
# '#F78017', # orange
# '#478010', # green
# '#53379B', # purple
# '#2060A7', # blue
# '#873770', # magenta
# '#D03050' # pink
# ]
colorslist = ['#007030', # dark green
'#F0D730', # yellow
'#C02717', # red
'#478010', # green
'#AAB71B', # lightgreen
'#F78017', # orange
'#40A787', # cyan'#
'#008797', # light blue
'#2060A7', # blue
'#D03050', # pink
'#53379B', # purple
'#873770', # magenta
]
import matplotlib.colors
colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
model_pos = {'Cb stellate':0, 'RS Inhibitory':1, 'FS':2, 'RS Pyramidal':3,
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':4,
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':5, 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':6,
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':7, 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':8,
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':9,
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':10, 'STN':11}
Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
# RS_p = mlines.Line2D([], [], color=colors[model_pos['RS Pyramidal']], marker=Markers[model_pos['RS Pyramidal']], markersize=marker_s_leg, linestyle='None',
# label='Model D')
# RS_i = mlines.Line2D([], [], color=colors[model_pos['RS Inhibitory']], marker=Markers[model_pos['RS Inhibitory']], markersize=marker_s_leg, linestyle='None',
# label='Model B')
# FS = mlines.Line2D([], [], color=colors[model_pos['FS']], marker=Markers[model_pos['FS']], markersize=marker_s_leg, linestyle='None', label='Model C')
# RS_p_Kv = mlines.Line2D([], [], color=colors[model_pos['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
# label='Model H')
# RS_i_Kv = mlines.Line2D([], [], color=colors[model_pos['RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
# label='Model E')
# FS_Kv = mlines.Line2D([], [], color=colors[model_pos['Cb stellate']], marker=Markers[model_pos['Cb stellate']], markersize=marker_s_leg, linestyle='None', label='Model G')
# Cb = mlines.Line2D([], [], color=colors[8], marker=Markers[8], markersize=marker_s_leg, linestyle='None',
# label='Model A')
# Cb_pl = mlines.Line2D([], [], color=colors[model_pos['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
# label='Model F')
# Cb_sw = mlines.Line2D([], [], color=colors[model_pos['Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
# label='Model J')
# STN = mlines.Line2D([], [], color=colors[model_pos['STN']], marker=Markers[model_pos['STN']], markersize=marker_s_leg, linestyle='None', label='Model L')
# STN_pl = mlines.Line2D([], [], color=colors[model_pos['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
# label='Model I')
# STN_sw = mlines.Line2D([], [], color=colors[model_pos['STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
# label='Model K')
RS_p = mlines.Line2D([], [], color='#007030', marker="^",
markersize=marker_s_leg, linestyle='None',
label='Model D')
RS_i = mlines.Line2D([], [], color='#F0D730', marker="o",
markersize=marker_s_leg, linestyle='None',
label='Model B')
FS = mlines.Line2D([], [], color='#C02717', marker="o", markersize=marker_s_leg,
linestyle='None', label='Model C')
RS_p_Kv = mlines.Line2D([], [], color='#478010',
marker="D",
markersize=marker_s_leg, linestyle='None',
label='Model H')
RS_i_Kv = mlines.Line2D([], [], color='#AAB71B',
marker="^",
markersize=marker_s_leg, linestyle='None',
label='Model E')
FS_Kv = mlines.Line2D([], [], color='#F78017',
marker="D", markersize=marker_s_leg,
linestyle='None', label='Model G')
Cb = mlines.Line2D([], [], color='#40A787', marker="o",
markersize=marker_s_leg, linestyle='None',
label='Model A')
Cb_pl = mlines.Line2D([], [], color='#008797',
marker="^",
markersize=marker_s_leg, linestyle='None',
label='Model F')
Cb_sw = mlines.Line2D([], [], color='#2060A7',
marker="s",
markersize=marker_s_leg, linestyle='None',
label='Model J')
STN = mlines.Line2D([], [], color='#D03050', marker="s", markersize=marker_s_leg,
linestyle='None', label='Model L')
STN_pl = mlines.Line2D([], [], color='#53379B',
marker="D",
markersize=marker_s_leg, linestyle='None',
label='Model I')
STN_sw = mlines.Line2D([], [], color='#873770',
marker="s",
markersize=marker_s_leg, linestyle='None',
label='Model K')
# ax.legend(handles=[RS_p, RS_i, FS, RS_p_Kv, RS_i_Kv, FS_Kv, Cb, Cb_pl, Cb_sw, STN, STN_pl, STN_sw], loc='center',
# bbox_to_anchor=pos, ncol=ncol, frameon=False)
ax.legend(handles=[Cb, RS_i, FS, RS_p, RS_i_Kv, Cb_pl, FS_Kv, RS_p_Kv, STN_pl, Cb_sw, STN_sw, STN], loc='center',
bbox_to_anchor=pos, ncol=ncol, frameon=False)
def plot_AUC_alt(ax, model='FS', color1='red', color2='dodgerblue', alteration='shift'):
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
model_names = ['RS Pyramidal','RS Inhibitory','FS',
'RS Pyramidal +$K_V1.1$','RS Inhibitory +$K_V1.1$',
'FS +$K_V1.1$','Cb stellate','Cb stellate +$K_V1.1$',
'Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$',
'STN $\Delta$$K_V1.1$']
model_name_dict = {'RS Pyramidal': 'RS Pyramidal',
'RS Inhibitory': 'RS Inhibitory',
'FS': 'FS',
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS Pyramidal +$K_V1.1$',
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS Inhibitory +$K_V1.1$',
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'FS +$K_V1.1$',
'Cb stellate': 'Cb stellate',
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb stellate +$K_V1.1$',
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb stellate $\Delta$$K_V1.1$',
'STN': 'STN',
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN +$K_V1.1$',
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN $\Delta$$K_V1.1$'}
colorslist = ['#007030', # dark green
'#F0D730', # yellow
'#C02717', # red
'#478010', # green
'#AAB71B', # lightgreen
'#F78017', # orange
'#40A787', # cyan'#
'#008797', # light blue
'#2060A7', # blue
'#D03050', # pink
'#53379B', # purple
'#873770', # magenta
]
import matplotlib.colors
colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
clr_dict = {}
for m in range(len(model_names)):
clr_dict[model_names[m]] = colors[m]
if alteration=='shift':
df = pd.read_csv('./Figures/Data/AUC_shift_ex.csv')
df = df.sort_values('alteration')
ax.set_xlabel('$\Delta$$V_{1/2}$')
elif alteration=='slope':
df = pd.read_csv('./Figures/Data/AUC_slope_ex.csv')
ax.set_xscale("log")
ax.set_xticks([0.5, 1, 2])
ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
ax.xaxis.set_minor_formatter(NullFormatter())
ax.set_xlabel('$k$/$k_{WT}$')
elif alteration=='g':
df = pd.read_csv('./Figures/Data/AUC_g_ex.csv')
ax.set_xscale("log")
ax.set_xticks([0.5, 1, 2])
ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
ax.xaxis.set_minor_formatter(NullFormatter())
ax.set_xlabel('$g$/$g_{WT}$')
for mod in model_names:
if mod == model_name_dict[model]:
ax.plot(df['alteration'], df[mod], color=clr_dict[mod], alpha=1, zorder=10, linewidth=2)
else:
ax.plot(df['alteration'], df[mod], color=clr_dict[mod],alpha=0.5, zorder=1, linewidth=1)
if alteration=='shift':
ax.set_ylabel('Normalized $\Delta$AUC', labelpad=4)
else:
ax.set_ylabel('Normalized $\Delta$AUC', labelpad=0)
x = df['alteration']
y = df[model_name_dict[model]]
ax.set_xlim(x.min(), x.max())
ax.set_ylim(df[model_names].min().min(), df[model_names].max().max())
# x axis color gradient
cvals = [-2., 2]
colors = ['lightgrey', 'k']
norm = plt.Normalize(min(cvals), max(cvals))
tuples = list(zip(map(norm, cvals), colors))
cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)
(xstart, xend) = ax.get_xlim()
(ystart, yend) = ax.get_ylim()
print(ystart, yend)
start = (xstart, ystart * 1.0)
end = (xend, ystart * 1.0)
ax = gradientaxis(ax, start, end, cmap, n=200, lw=4)
ax.spines['bottom'].set_visible(False)
return ax
def plot_fI(ax, model='RS Pyramidal', type='shift', alt='m', color1='red', color2='dodgerblue'):
model_save_name = {'RS Pyramidal': 'RS_pyr_posp',
'RS Inhibitory': 'RS_inhib_posp',
'FS': 'FS_posp',
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS_pyr_Kv',
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS_inhib_Kv',
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'FS_Kv',
'Cb stellate': 'Cb_stellate',
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb_stellate_Kv',
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb_stellate_Kv_only',
'STN': 'STN',
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN_Kv',
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN_Kv_only'}
cvals = [-2., 2]
colors = [color1, color2]
norm = plt.Normalize(min(cvals), max(cvals))
tuples = list(zip(map(norm, cvals), colors))
cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)
colors = cmap(np.linspace(0, 1, 22))
df = pd.read_csv('./Figures/Data/Model_fI/{}_fI.csv'.format(model_save_name[model]))
df.drop(['Unnamed: 0'], axis=1)
newdf = df.loc[df.index[(df['alt'] == alt) & (df['type'] == type)], :]
newdf['mag'] = newdf['mag'].astype('float')
newdf = newdf.sort_values('mag').reset_index()
c = 0
for i in newdf.index:
ax.plot(json.loads(newdf.loc[i, 'I']), json.loads(newdf.loc[i, 'F']), color=colors[c])
c += 1
ax.set_ylabel('Frequency [Hz]')
ax.set_xlabel('Current [nA]')
if model == 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':
ax.set_title("Model G", x=0.2, y=1.0)
elif model == 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':
ax.set_title("Model I", x=0.2, y=1.0)
else:
ax.set_title("", x=0.2, y=1.0)
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
L = ax.get_ylim()
ax.set_ylim([0, L[1]])
return ax
#%%
boxplot_style()
color_dict = {'Cb stellate': '#40A787', # cyan'#
'RS Inhibitory': '#F0D730', # yellow
'FS': '#C02717', # red
'RS Pyramidal': '#007030', # dark green
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#AAB71B', # lightgreen
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#008797', # light blue
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#F78017', # orange
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#478010', # green
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#53379B', # purple
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#2060A7', # blue
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#873770', # magenta
'STN': '#D03050' # pink
}
model_letter = {
'Cb stellate': 'A',
'RS Inhibitory': 'B',
'FS': 'C',
'RS Pyramidal': 'D',
'RS Inhibitory': 'E',
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'F',
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'G',
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'H',
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'I',
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'J',
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'K',
'STN':'L',
}
# plot setup
marker_s_leg = 2
max_width = 20
pad_x = 0.85
pad_y= 0.4
pad_w = 1.1
pad_h = 0.7
fig = plt.figure()
gs = fig.add_gridspec(3, 7, wspace=1.2, hspace=1.)
ax0 = fig.add_subplot(gs[0,2:7])
ax0_ex = fig.add_subplot(gs[0,1])
ax0_fI = fig.add_subplot(gs[0,0])
ax1 = fig.add_subplot(gs[1,2:7])
ax1_ex = fig.add_subplot(gs[1,1])
ax1_fI = fig.add_subplot(gs[1,0])
ax2 = fig.add_subplot(gs[2,2:7])
ax2_ex = fig.add_subplot(gs[2,1])
ax2_fI = fig.add_subplot(gs[2,0])
line_width = 1
# plot fI examples
ax0_fI = plot_fI(ax0_fI, model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='shift', alt='s', color1='lightgrey', color2='k')
rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1 + pad_h, fill=False, lw=line_width,transform=ax0_fI.transAxes, color=color_dict['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
rec = ax0_fI.add_patch(rec)
rec.set_clip_on(False)
ax1_fI = plot_fI(ax1_fI, model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='slope', alt='u', color1='lightgrey', color2='k')
rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1 + pad_h, fill=False, lw=line_width,transform=ax1_fI.transAxes, color=color_dict['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
rec = ax1_fI.add_patch(rec)
rec.set_clip_on(False)
ax2_fI = plot_fI(ax2_fI, model='STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='g', alt='Leak', color1='lightgrey', color2='k')
rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1 + pad_h, fill=False, lw=line_width,transform=ax2_fI.transAxes, color=color_dict['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
rec = ax2_fI.add_patch(rec)
rec.set_clip_on(False)
# plot boxplots
boxplot_with_markers(ax0,max_width, alteration='shift')
boxplot_with_markers(ax1,max_width, alteration='slope')
boxplot_with_markers(ax2,max_width, alteration='g')
# plot legend
pos = (0.225, -0.9)
ncol = 6
model_legend(ax2, marker_s_leg, pos, ncol)
# plot examples
plot_AUC_alt(ax0_ex,model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k', alteration='shift')
plot_AUC_alt(ax1_ex,model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k',alteration='slope')
plot_AUC_alt(ax2_ex, model='STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k', alteration='g')
# label subplots with letters
ax0_fI.text(-0.875, 1.35, string.ascii_uppercase[0], transform=ax0_fI.transAxes, size=10, weight='bold')
ax0_ex.text(-0.8, 1.35, string.ascii_uppercase[1], transform=ax0_ex.transAxes, size=10, weight='bold')
ax0.text(-0.075, 1.35, string.ascii_uppercase[2], transform=ax0.transAxes, size=10, weight='bold')
ax1_fI.text(-0.875, 1.35, string.ascii_uppercase[3], transform=ax1_fI.transAxes,size=10, weight='bold')
ax1_ex.text(-0.8, 1.35, string.ascii_uppercase[4], transform=ax1_ex.transAxes, size=10, weight='bold')
ax1.text(-0.075, 1.35, string.ascii_uppercase[5], transform=ax1.transAxes, size=10, weight='bold')
ax2_fI.text(-0.875, 1.35, string.ascii_uppercase[6], transform=ax2_fI.transAxes,size=10, weight='bold')
ax2_ex.text(-0.8, 1.35, string.ascii_uppercase[7], transform=ax2_ex.transAxes, size=10, weight='bold')
ax2.text(-0.075, 1.35, string.ascii_uppercase[8], transform=ax2.transAxes, size=10, weight='bold')
#save
fig.set_size_inches(cm2inch(20.75,12))
fig.savefig('./Figures/AUC_correlation.pdf', dpi=fig.dpi) #pdf #eps
# fig.savefig('./Figures/AUC_correlation.png', dpi=fig.dpi) #pdf #eps
plt.show()

322
Figures/ramp_firing_letters.py Normal file
View File

@ -0,0 +1,322 @@
# # plot ramp protocol and responses of each model to ramp
# import numpy as np
# import pandas as pd
# import matplotlib.pyplot as plt
# import matplotlib.gridspec as gridspec
# from matplotlib.transforms import Bbox
# import string
#
# def cm2inch(*tupl):
# inch = 2.54
# if isinstance(tupl[0], tuple):
# return tuple(i/inch for i in tupl[0])
# else:
# return tuple(i/inch for i in tupl)
#
# #### from https://gist.github.com/dmeliza/3251476 #####################################################################
# from matplotlib.offsetbox import AnchoredOffsetbox
# class AnchoredScaleBar(AnchoredOffsetbox):
# def __init__(self, transform, sizex=0, sizey=0, labelx=None, labely=None, loc=4,
# pad=0.1, borderpad=0.1, sep=2, prop=None, barcolor="black", barwidth=None,
# **kwargs):
# """
# Draw a horizontal and/or vertical bar with the size in data coordinate
# of the give axes. A label will be drawn underneath (center-aligned).
# - transform : the coordinate frame (typically axes.transData)
# - sizex,sizey : width of x,y bar, in data units. 0 to omit
# - labelx,labely : labels for x,y bars; None to omit
# - loc : position in containing axes
# - pad, borderpad : padding, in fraction of the legend font size (or prop)
# - sep : separation between labels and bars in points.
# - **kwargs : additional arguments passed to base class constructor
# """
# from matplotlib.patches import Rectangle
# from matplotlib.offsetbox import AuxTransformBox, VPacker, HPacker, TextArea, DrawingArea
# bars = AuxTransformBox(transform)
# if sizex:
# bars.add_artist(Rectangle((0, 0), sizex, 0, ec=barcolor, lw=barwidth, fc="none"))
# if sizey:
# bars.add_artist(Rectangle((0, 0), 0, sizey, ec=barcolor, lw=barwidth, fc="none"))
#
# if sizex and labelx:
# self.xlabel = TextArea(labelx)
# bars = VPacker(children=[bars, self.xlabel], align="center", pad=0, sep=sep)
# if sizey and labely:
# self.ylabel = TextArea(labely)
# bars = HPacker(children=[self.ylabel, bars], align="center", pad=0, sep=sep)
#
# AnchoredOffsetbox.__init__(self, loc, pad=pad, borderpad=borderpad,
# child=bars, prop=prop, frameon=False, **kwargs)
#
#
# def add_scalebar(ax, matchx=True, matchy=True, hidex=True, hidey=True, **kwargs):
# """ Add scalebars to axes
# Adds a set of scale bars to *ax*, matching the size to the ticks of the plot
# and optionally hiding the x and y axes
# - ax : the axis to attach ticks to
# - matchx,matchy : if True, set size of scale bars to spacing between ticks
# if False, size should be set using sizex and sizey params
# - hidex,hidey : if True, hide x-axis and y-axis of parent
# - **kwargs : additional arguments passed to AnchoredScaleBars
# Returns created scalebar object
# """
#
# def f(axis):
# l = axis.get_majorticklocs()
# return len(l) > 1 and (l[1] - l[0])
#
# if matchx:
# kwargs['sizex'] = f(ax.xaxis)
# kwargs['labelx'] = str(kwargs['sizex'])
# if matchy:
# kwargs['sizey'] = f(ax.yaxis)
# kwargs['labely'] = str(kwargs['sizey'])
#
# sb = AnchoredScaleBar(ax.transData, **kwargs)
# ax.add_artist(sb)
#
# if hidex: ax.xaxis.set_visible(False)
# if hidey: ax.yaxis.set_visible(False)
# if hidex and hidey: ax.set_frame_on(False)
#
# return sb
# ########################################################################################################################
#
#
# def plot_ramp_V(ax, model='RS Pyramidal'): # , stop=750
# model_ramp = pd.read_csv('./Figures/Data/model_ramp.csv')
# ax.plot(model_ramp['t'], model_ramp[model], 'k', linewidth=0.025)
# ax.set_ylabel('V')
# ax.set_xlabel('Time [s]')
# ax.set_ylim(-80, 60)
# ax.axis('off')
# ax.set_title(model)
#
# #% plot setup
# fig = plt.figure(figsize=cm2inch(17.6,17.6))
#
# gs0 = fig.add_gridspec(3, 2, wspace=0.1)
# gs00 = gs0[:,0].subgridspec(7, 2, wspace=0.6, hspace=1)
# gs01 = gs0[:,1].subgridspec(7, 2, wspace=0.6, hspace=1)
#
# ax1_ramp = fig.add_subplot(gs00[0,0:2])
# ax2_ramp = fig.add_subplot(gs01[0,0:2])
# ax3_ramp = fig.add_subplot(gs00[1,0:2])
# ax4_ramp = fig.add_subplot(gs01[1,0:2])
# ax5_ramp = fig.add_subplot(gs00[2, 0:2])
# ax6_ramp = fig.add_subplot(gs01[2, 0:2])
# ax7_ramp = fig.add_subplot(gs00[3,0:2])
# ax8_ramp = fig.add_subplot(gs01[3,0:2])
# ax9_ramp = fig.add_subplot(gs00[4,0:2])
# ax10_ramp = fig.add_subplot(gs01[4,0:2])
# ax11_ramp = fig.add_subplot(gs00[5,0:2])
# ax12_ramp = fig.add_subplot(gs01[5,0:2])
#
# ramp_axs = [ax1_ramp, ax2_ramp, ax3_ramp, ax4_ramp, ax5_ramp,ax6_ramp, ax7_ramp, ax8_ramp,
# ax9_ramp, ax10_ramp, ax11_ramp, ax12_ramp]
#
# # order of models
# models = ['Cb stellate','RS Inhibitory','FS', 'RS Pyramidal','RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
# 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
# 'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
# 'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
# 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN']
#
# # plot ramps
# for i in range(len(models)):
# plot_ramp_V(ramp_axs[i], model=models[i])
#
# # add scalebar
# plt.rcParams.update({'font.size': 6})
#
# add_scalebar(ax11_ramp, matchx=False, matchy=False, hidex=True, hidey=True, sizex=1000, sizey=50, labelx='1 s',
# labely='50 mV', loc=3, pad=-2, borderpad=0, barwidth=1, bbox_to_anchor=Bbox.from_bounds(-0.05, 0.1, 1, 1),
# bbox_transform=ax11_ramp.transAxes)
# # add_scalebar(ax12_ramp, matchx=False, matchy=False, hidex=True, hidey=True, sizex=1000, sizey=25, labelx='1 s',
# # labely='25 mV', loc=3, pad=-2, borderpad=0, barwidth=2, bbox_to_anchor=Bbox.from_bounds(-0.05, 0.1, 1, 1),
# # bbox_transform=ax12_ramp.transAxes)
#
# # add subplot labels
# for i in range(0,len(models)):
# ramp_axs[i].text(-0.05, 1.08, string.ascii_uppercase[i], transform=ramp_axs[i].transAxes, size=10, weight='bold')
#
# fig.savefig('./Figures/ramp_firing.pdf', dpi=3000)
# plt.show()
# plot ramp protocol and responses of each model to ramp
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.transforms import Bbox
import string
def cm2inch(*tupl):
inch = 2.54
if isinstance(tupl[0], tuple):
return tuple(i/inch for i in tupl[0])
else:
return tuple(i/inch for i in tupl)
#### from https://gist.github.com/dmeliza/3251476 #####################################################################
from matplotlib.offsetbox import AnchoredOffsetbox
class AnchoredScaleBar(AnchoredOffsetbox):
def __init__(self, transform, sizex=0, sizey=0, labelx=None, labely=None, loc=4,
pad=0.1, borderpad=0.1, sep=2, prop=None, barcolor="black", barwidth=None,
**kwargs):
"""
Draw a horizontal and/or vertical bar with the size in data coordinate
of the give axes. A label will be drawn underneath (center-aligned).
- transform : the coordinate frame (typically axes.transData)
- sizex,sizey : width of x,y bar, in data units. 0 to omit
- labelx,labely : labels for x,y bars; None to omit
- loc : position in containing axes
- pad, borderpad : padding, in fraction of the legend font size (or prop)
- sep : separation between labels and bars in points.
- **kwargs : additional arguments passed to base class constructor
"""
from matplotlib.patches import Rectangle
from matplotlib.offsetbox import AuxTransformBox, VPacker, HPacker, TextArea, DrawingArea
bars = AuxTransformBox(transform)
if sizex:
bars.add_artist(Rectangle((0, 0), sizex, 0, ec=barcolor, lw=barwidth, fc="none"))
if sizey:
bars.add_artist(Rectangle((0, 0), 0, sizey, ec=barcolor, lw=barwidth, fc="none"))
if sizex and labelx:
self.xlabel = TextArea(labelx)
bars = VPacker(children=[bars, self.xlabel], align="center", pad=0, sep=sep)
if sizey and labely:
self.ylabel = TextArea(labely)
bars = HPacker(children=[self.ylabel, bars], align="center", pad=0, sep=sep)
AnchoredOffsetbox.__init__(self, loc, pad=pad, borderpad=borderpad,
child=bars, prop=prop, frameon=False, **kwargs)
def add_scalebar(ax, matchx=True, matchy=True, hidex=True, hidey=True, **kwargs):
""" Add scalebars to axes
Adds a set of scale bars to *ax*, matching the size to the ticks of the plot
and optionally hiding the x and y axes
- ax : the axis to attach ticks to
- matchx,matchy : if True, set size of scale bars to spacing between ticks
if False, size should be set using sizex and sizey params
- hidex,hidey : if True, hide x-axis and y-axis of parent
- **kwargs : additional arguments passed to AnchoredScaleBars
Returns created scalebar object
"""
def f(axis):
l = axis.get_majorticklocs()
return len(l) > 1 and (l[1] - l[0])
if matchx:
kwargs['sizex'] = f(ax.xaxis)
kwargs['labelx'] = str(kwargs['sizex'])
if matchy:
kwargs['sizey'] = f(ax.yaxis)
kwargs['labely'] = str(kwargs['sizey'])
sb = AnchoredScaleBar(ax.transData, **kwargs)
ax.add_artist(sb)
if hidex: ax.xaxis.set_visible(False)
if hidey: ax.yaxis.set_visible(False)
if hidex and hidey: ax.set_frame_on(False)
return sb
########################################################################################################################
def plot_ramp_V(ax, model='RS Pyramidal'): # , stop=750
model_ramp = pd.read_csv('./Figures/Data/model_ramp.csv')
# ax.plot(model_ramp['t'], model_ramp[model], 'k', linewidth=0.0025)
ax.plot(model_ramp['t'], model_ramp[model], 'k', linewidth=0.1)
ax.set_ylabel('V')
ax.set_xlabel('Time [s]')
ax.set_ylim(-80, 60)
ax.axis('off')
ax.set_title(model, fontsize=8)
def plot_I_ramp(ax):
dt = 0.01
I_low = 0
I_high = 0.001
initial_period = 1000
sec = 4
ramp_len = int(4 * 1000 * 1 / dt)
stim_time = ramp_len * 2
I_amp = np.array([0])
I_amp = np.reshape(I_amp, (1, I_amp.shape[0]))
I_ramp = np.zeros((stim_time, 1)) @ I_amp
I_ramp[:, :] = np.ones((stim_time, 1)) @ I_amp
stim_num_step = I_ramp.shape[1]
start=0
I_ramp[start:int(start + ramp_len), 0] = np.linspace(0, I_high, ramp_len)
I_ramp[int(start + ramp_len):int(start + ramp_len * 2), 0] = np.linspace(I_high, 0, ramp_len)
t = np.arange(0, 4000 * 2, dt)
ax.plot(t, I_ramp)
ax.set_ylabel('I')
ax.set_xlabel('Time [s]')
ax.axis('off')
ax.set_title('Ramp Current', fontsize=8, x=0.5, y=-0.5)
return ax
#% plot setup
fig = plt.figure(figsize=cm2inch(17.6,25))
gs0 = fig.add_gridspec(2, 1, wspace=0.)
gs00 = gs0[:].subgridspec(13, 1, wspace=0.7, hspace=1.0)
ax1_ramp = fig.add_subplot(gs00[0])
ax2_ramp = fig.add_subplot(gs00[1])
ax3_ramp = fig.add_subplot(gs00[2])
ax4_ramp = fig.add_subplot(gs00[3])
ax5_ramp = fig.add_subplot(gs00[4])
ax6_ramp = fig.add_subplot(gs00[5])
ax7_ramp = fig.add_subplot(gs00[6])
ax8_ramp = fig.add_subplot(gs00[7])
ax9_ramp = fig.add_subplot(gs00[8])
ax10_ramp = fig.add_subplot(gs00[9])
ax11_ramp = fig.add_subplot(gs00[10])
ax12_ramp = fig.add_subplot(gs00[11])
ax13_I = fig.add_subplot(gs00[12])
ramp_axs = [ax1_ramp, ax2_ramp, ax3_ramp, ax4_ramp, ax5_ramp,ax6_ramp, ax7_ramp, ax8_ramp,
ax9_ramp, ax10_ramp, ax11_ramp, ax12_ramp]
# order of models
models = ['Cb stellate','RS Inhibitory','FS', 'RS Pyramidal','RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN']
# plot ramps
for i in range(len(models)):
plot_ramp_V(ramp_axs[i], model=models[i])
# add scalebar
plt.rcParams.update({'font.size': 6})
add_scalebar(ax12_ramp, matchx=False, matchy=False, hidex=True, hidey=True, sizex=1000, sizey=50, labelx='1 s',
labely='50 mV', loc=3, pad=-2, borderpad=0, barwidth=1, bbox_to_anchor=Bbox.from_bounds(0.01, 0.05, 1, 1),
bbox_transform=ax12_ramp.transAxes)
ax13_I = plot_I_ramp(ax13_I)
add_scalebar(ax13_I, matchx=False, matchy=False, hidex=True, hidey=True, sizex=1000, sizey=0.0005, labelx='1 s',
labely='0.5 $I_{max}$', loc=3, pad=-2, borderpad=0, barwidth=1,
bbox_to_anchor=Bbox.from_bounds(0.0, -0.01, 1, 1), bbox_transform=ax13_I.transAxes)
# add subplot labels
for i in range(0,len(models)):
ramp_axs[i].text(-0.01, 1.1, string.ascii_uppercase[i], transform=ramp_axs[i].transAxes, size=10, weight='bold')
#save
fig.set_size_inches(cm2inch(17.6,22))
fig.savefig('./Figures/ramp_firing.png', dpi=fig.dpi)#pdf #eps
plt.show()

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57
Figures/rheobase_correlation.py
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@ -157,6 +157,8 @@ def boxplot_with_markers(ax,max_width, alteration='shift', msize=2.2):
clr_dict = {} clr_dict = {}
for m in range(len(model_names)): for m in range(len(model_names)):
clr_dict[model_names[m]] = colors[m] clr_dict[model_names[m]] = colors[m]
print(colors)
print(clr_dict)
Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"] Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
if alteration=='shift': if alteration=='shift':
i = 3 # Kv1.1 act i = 3 # Kv1.1 act
@ -217,27 +219,52 @@ def boxplot_with_markers(ax,max_width, alteration='shift', msize=2.2):
def model_legend(ax, marker_s_leg, pos, ncol): def model_legend(ax, marker_s_leg, pos, ncol):
colorslist = [ '#40A787', # cyan'# # colorslist = [ '#40A787', # cyan'#
'#F0D730', # yellow # '#F0D730', # yellow
'#C02717', # red # '#C02717', # red
'#007030', # dark green # '#007030', # dark green
'#AAB71B', # lightgreen # '#AAB71B', # lightgreen
'#008797', # light blue # '#008797', # light blue
'#F78017', # orange # '#F78017', # orange
'#478010', # green # '#478010', # green
'#53379B', # purple # '#53379B', # purple
'#2060A7', # blue # '#2060A7', # blue
'#873770', # magenta # '#873770', # magenta
'#D03050' # pink # '#D03050' # pink
] # ]
import matplotlib.colors # import matplotlib.colors
colors = [matplotlib.colors.to_rgb(c) for c in colorslist] # colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
model_pos = {'Cb stellate':0, 'RS Inhibitory':1, 'FS':2, 'RS Pyramidal':3, model_pos = {'Cb stellate':0, 'RS Inhibitory':1, 'FS':2, 'RS Pyramidal':3,
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':4, 'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':4,
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':5, 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':6, 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':5, 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':6,
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':7, 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':8, 'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':7, 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':8,
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':9, 'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':9,
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':10, 'STN':11} 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':10, 'STN':11}
# model_pos = {'Cb stellate': 0, 'RS Inhibitory': 1, 'FS': 2, 'RS Pyramidal': 3,
# 'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 4,
# 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 5,
# 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 6,
# 'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 7,
# 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 8,
# 'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 9,
# 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 10, 'STN': 11}
colorslist = ['#007030', # dark green
'#F0D730', # yellow
'#C02717', # red
'#478010', # green
'#AAB71B', # lightgreen
'#F78017', # orange
'#40A787', # cyan'#
'#008797', # light blue
'#2060A7', # blue
'#D03050', # pink
'#53379B', # purple
'#873770', # magenta
]
import matplotlib.colors
colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"] Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
RS_p = mlines.Line2D([], [], color=colors[model_pos['RS Pyramidal']], marker=Markers[model_pos['RS Pyramidal']], markersize=marker_s_leg, linestyle='None', RS_p = mlines.Line2D([], [], color=colors[model_pos['RS Pyramidal']], marker=Markers[model_pos['RS Pyramidal']], markersize=marker_s_leg, linestyle='None',
label='RS pyramidal') label='RS pyramidal')

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# -*- coding: utf-8 -*-
"""
Created on Sat Jul 3 19:52:04 2021
@author: nils
"""
import pandas as pd
import numpy as np
import string
import textwrap
import json
import matplotlib
import matplotlib.lines as mlines
from matplotlib import ticker
from matplotlib.ticker import NullFormatter
from Figures.plotstyle import boxplot_style
def cm2inch(*tupl):
inch = 2.54
if isinstance(tupl[0], tuple):
return tuple(i/inch for i in tupl[0])
else:
return tuple(i/inch for i in tupl)
#%% ##################### From https://stackoverflow.com/questions/52878845/swarmplot-with-hue-affecting-marker-beyond-color ##
# to change marker types in seaborn swarmplot
import seaborn as sns
import matplotlib.pyplot as plt
############## Begin hack ##############
from matplotlib.axes._axes import Axes
from matplotlib.markers import MarkerStyle
from numpy import ndarray
def GetColor2Marker(markers):
colorslist = ['#40A787', # cyan'#
'#F0D730', # yellow
'#C02717', # red
'#007030', # dark green
'#AAB71B', # lightgreen
'#008797', # light blue
'#F78017', # orange
'#478010', # green
'#53379B', # purple
'#2060A7', # blue
'#873770', # magenta
'#D03050' # pink
]
import matplotlib.colors
palette = [matplotlib.colors.to_rgb(c) for c in colorslist] #
mkcolors = [(palette[i]) for i in range(len(markers))]
return dict(zip(mkcolors,markers))
def fixlegend(ax,markers,markersize=3,**kwargs):
# Fix Legend
legtitle = ax.get_legend().get_title().get_text()
_,l = ax.get_legend_handles_labels()
colorslist = ['#40A787', # cyan'#
'#F0D730', # yellow
'#C02717', # red
'#007030', # dark green
'#AAB71B', # lightgreen
'#008797', # light blue
'#F78017', # orange
'#478010', # green
'#53379B', # purple
'#2060A7', # blue
'#873770', # magenta
'#D03050' # pink
]
import matplotlib.colors
palette = [matplotlib.colors.to_rgb(c) for c in colorslist]
mkcolors = [(palette[i]) for i in range(len(markers))]
newHandles = [plt.Line2D([0],[0], ls="none", marker=m, color=c, mec="none", markersize=markersize,**kwargs) \
for m,c in zip(markers, mkcolors)]
ax.legend(newHandles,l)
leg = ax.get_legend()
leg.set_title(legtitle)
old_scatter = Axes.scatter
def new_scatter(self, *args, **kwargs):
colors = kwargs.get("c", None)
co2mk = kwargs.pop("co2mk",None)
FinalCollection = old_scatter(self, *args, **kwargs)
if co2mk is not None and isinstance(colors, ndarray):
Color2Marker = GetColor2Marker(co2mk)
paths=[]
for col in colors:
mk=Color2Marker[tuple(col)]
marker_obj = MarkerStyle(mk)
paths.append(marker_obj.get_path().transformed(marker_obj.get_transform()))
FinalCollection.set_paths(paths)
return FinalCollection
Axes.scatter = new_scatter
############## End hack. ##############
########################################################################################################################
#%% add gradient arrows
import matplotlib.pyplot as plt
import matplotlib.transforms
import matplotlib.path
from matplotlib.collections import LineCollection
def rainbowarrow(ax, start, end, cmap, n=50,lw=3):
# Arrow shaft: LineCollection
x = np.linspace(start[0],end[0],n)
y = np.linspace(start[1],end[1],n)
points = np.array([x,y]).T.reshape(-1,1,2)
segments = np.concatenate([points[:-1],points[1:]], axis=1)
lc = LineCollection(segments, cmap=cmap, linewidth=lw)
lc.set_array(np.linspace(0,1,n))
ax.add_collection(lc)
# Arrow head: Triangle
tricoords = [(0,-0.02),(0.025,0),(0,0.02),(0,-0.02)]
angle = np.arctan2(end[1]-start[1],end[0]-start[0])
rot = matplotlib.transforms.Affine2D().rotate(angle)
tricoords2 = rot.transform(tricoords)
tri = matplotlib.path.Path(tricoords2, closed=True)
ax.scatter(end[0],end[1], c=1, s=(4*lw)**2, marker=tri, cmap=cmap,vmin=0)
ax.autoscale_view()
return ax
def gradientaxis(ax, start, end, cmap, n=100,lw=1):
# Arrow shaft: LineCollection
x = np.linspace(start[0],end[0],n)
y = np.linspace(start[1],end[1],n)
points = np.array([x,y]).T.reshape(-1,1,2)
segments = np.concatenate([points[:-1],points[1:]], axis=1)
lc = LineCollection(segments, cmap=cmap, linewidth=lw,zorder=15)
lc.set_array(np.linspace(0,1,n))
ax.add_collection(lc)
return ax
#%%
def boxplot_with_markers(ax,max_width, alteration='shift', msize=2.2):
hlinewidth = 0.5
model_names = ['RS pyramidal','RS inhibitory','FS',
'RS pyramidal +$K_V1.1$','RS inhibitory +$K_V1.1$',
'FS +$K_V1.1$','Cb stellate','Cb stellate +$K_V1.1$',
'Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$',
'STN $\Delta$$K_V1.1$']
colorslist = ['#007030', # dark green
'#F0D730', # yellow
'#C02717', # red
'#478010', # green
'#AAB71B', # lightgreen
'#F78017', # orange
'#40A787', # cyan'#
'#008797', # light blue
'#2060A7', # blue
'#D03050', # pink
'#53379B', # purple
'#873770', # magenta
]
import matplotlib.colors
colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
clr_dict = {}
for m in range(len(model_names)):
clr_dict[model_names[m]] = colors[m]
Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
if alteration=='shift':
i = 3 # Kv1.1 act
ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor='k')
df = pd.read_csv('./Figures/Data/rheo_shift_corr.csv')
sns.swarmplot(y="corr", x="$\Delta V_{1/2}$", hue="model", data=df,
palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
order=['Na activation', 'Na inactivation', 'K activation', '$K_V1.1$ activation',
'$K_V1.1$ inactivation', 'A activation', 'A inactivation'],
hue_order=model_names, co2mk=Markers)
lim = ax.get_xlim()
ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
ax.set_title("Shift ($\Delta V_{1/2}$)", y=1.05)
ax.set_xticklabels(['Na \nactivation', 'Na \ninactivation', 'K \nactivation', '$K_V1.1$ \nactivation',
'$K_V1.1$ \ninactivation', 'A \nactivation', 'A \ninactivation'])
elif alteration=='slope':
i = 4 # Kv1.1 inact
ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor='k')
df = pd.read_csv('./Figures/Data/rheo_scale_corr.csv')
# Add in points to show each observation
sns.swarmplot(y="corr", x="Slope (k)", hue="model", data=df,
palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
order=['Na activation', 'Na inactivation', 'K activation', '$K_V1.1$ activation',
'$K_V1.1$ inactivation', 'A activation', 'A inactivation'],
hue_order=model_names, co2mk=Markers)
lim = ax.get_xlim()
ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
ax.set_title("Slope (k)", y=1.05)
ax.set_xticklabels(['Na \nactivation', 'Na \ninactivation', 'K \nactivation', '$K_V1.1$ \nactivation',
'$K_V1.1$ \ninactivation', 'A \nactivation', 'A \ninactivation'])
elif alteration=='g':
i = 4 # Leak
ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor='k')
df = pd.read_csv('./Figures/Data/rheo_g_corr.csv')
# Add in points to show each observation
sns.swarmplot(y="corr", x="g", hue="model", data=df,
palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
order=['Na', 'K', '$K_V1.1$', 'A', 'Leak'],
hue_order=model_names, co2mk=Markers)
lim = ax.get_xlim()
ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
ax.set_title("Conductance (g)", y=1.05)
ax.set_xticklabels(textwrap.fill(x.get_text(), max_width) for x in ax.get_xticklabels())
else:
print('Please chose "shift", "slope" or "g"')
ax.get_legend().remove()
ax.xaxis.grid(False)
sns.despine(trim=True, bottom=True, ax=ax)
ax.set(xlabel=None, ylabel=r'Kendall $\it{\tau}$')
def model_legend(ax, marker_s_leg, pos, ncol):
colorslist = [ '#40A787', # cyan'#
'#F0D730', # yellow
'#C02717', # red
'#007030', # dark green
'#AAB71B', # lightgreen
'#008797', # light blue
'#F78017', # orange
'#478010', # green
'#53379B', # purple
'#2060A7', # blue
'#873770', # magenta
'#D03050' # pink
]
model_names = ['RS pyramidal', 'RS inhibitory', 'FS',
'RS pyramidal +$K_V1.1$', 'RS inhibitory +$K_V1.1$',
'FS +$K_V1.1$', 'Cb stellate', 'Cb stellate +$K_V1.1$',
'Cb stellate $\Delta$$K_V1.1$', 'STN', 'STN +$K_V1.1$',
'STN $\Delta$$K_V1.1$']
# colorslist = ['#007030', # dark green
# '#F0D730', # yellow
# '#C02717', # red
# '#478010', # green
# '#AAB71B', # lightgreen
# '#F78017', # orange
# '#40A787', # cyan'#
# '#008797', # light blue
# '#2060A7', # blue
# '#D03050', # pink
# '#53379B', # purple
# '#873770', # magenta
# ]
import matplotlib.colors
colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
clr_dict = {}
for m in range(len(model_names)):
clr_dict[model_names[m]] = colors[m]
import matplotlib.colors
colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
# model_pos = {'Cb stellate':0, 'RS Inhibitory':1, 'FS':2, 'RS Pyramidal':3,
# 'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':4,
# 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':5, 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':6,
# 'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':7, 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':8,
# 'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':9,
# 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':10, 'STN':11}
Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
model_pos = {'RS Pyramidal':0, 'RS Inhibitory':1, 'FS':2,
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':3, 'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':4,
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':5, 'Cb stellate':6, 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':7,
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':8, 'STN':9, 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':10,
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':11}
RS_p = mlines.Line2D([], [], color='#007030', marker="^",
markersize=marker_s_leg, linestyle='None',
label='Model D')
RS_i = mlines.Line2D([], [], color='#F0D730', marker="o",
markersize=marker_s_leg, linestyle='None',
label='Model B')
FS = mlines.Line2D([], [], color='#C02717', marker="o", markersize=marker_s_leg,
linestyle='None', label='Model C')
RS_p_Kv = mlines.Line2D([], [], color='#478010',
marker="D",
markersize=marker_s_leg, linestyle='None',
label='Model H')
RS_i_Kv = mlines.Line2D([], [], color='#AAB71B',
marker="^",
markersize=marker_s_leg, linestyle='None',
label='Model E')
FS_Kv = mlines.Line2D([], [], color='#F78017',
marker="D", markersize=marker_s_leg,
linestyle='None', label='Model G')
Cb = mlines.Line2D([], [], color='#40A787', marker="o",
markersize=marker_s_leg, linestyle='None',
label='Model A')
Cb_pl = mlines.Line2D([], [], color='#008797',
marker="^",
markersize=marker_s_leg, linestyle='None',
label='Model F')
Cb_sw = mlines.Line2D([], [], color='#2060A7',
marker="s",
markersize=marker_s_leg, linestyle='None',
label='Model J')
STN = mlines.Line2D([], [], color='#D03050', marker="s", markersize=marker_s_leg,
linestyle='None', label='Model L')
STN_pl = mlines.Line2D([], [], color='#53379B',
marker="D",
markersize=marker_s_leg, linestyle='None',
label='Model I')
STN_sw = mlines.Line2D([], [], color='#873770',
marker="s",
markersize=marker_s_leg, linestyle='None',
label='Model K')
#
# RS_p = mlines.Line2D([], [], color=colors[model_pos['RS Pyramidal']], marker=Markers[model_pos['RS Pyramidal']],
# markersize=marker_s_leg, linestyle='None',
# label='Model D')
# RS_i = mlines.Line2D([], [], color=colors[model_pos['RS Inhibitory']], marker=Markers[model_pos['RS Inhibitory']],
# markersize=marker_s_leg, linestyle='None',
# label='Model B')
# FS = mlines.Line2D([], [], color=colors[model_pos['FS']], marker=Markers[model_pos['FS']], markersize=marker_s_leg,
# linestyle='None', label='Model C')
# RS_p_Kv = mlines.Line2D([], [], color=colors[model_pos['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
# marker=Markers[model_pos['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
# markersize=marker_s_leg, linestyle='None',
# label='Model H')
# RS_i_Kv = mlines.Line2D([], [], color=colors[model_pos['RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
# marker=Markers[model_pos['RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
# markersize=marker_s_leg, linestyle='None',
# label='Model E')
# FS_Kv = mlines.Line2D([], [], color=colors[model_pos['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
# marker=Markers[model_pos['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg,
# linestyle='None', label='Model G')
# Cb = mlines.Line2D([], [], color=colors[model_pos['Cb stellate']], marker=Markers[model_pos['Cb stellate']],
# markersize=marker_s_leg, linestyle='None',
# label='Model A')
# Cb_pl = mlines.Line2D([], [], color=colors[model_pos['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
# marker=Markers[model_pos['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
# markersize=marker_s_leg, linestyle='None',
# label='Model F')
# Cb_sw = mlines.Line2D([], [], color=colors[model_pos['Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
# marker=Markers[model_pos['Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
# markersize=marker_s_leg, linestyle='None',
# label='Model J')
# STN = mlines.Line2D([], [], color=colors[model_pos['STN']], marker=Markers[model_pos['STN']], markersize=marker_s_leg,
# linestyle='None', label='Model L')
# STN_pl = mlines.Line2D([], [], color=colors[model_pos['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
# marker=Markers[model_pos['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
# markersize=marker_s_leg, linestyle='None',
# label='Model I')
# STN_sw = mlines.Line2D([], [], color=colors[model_pos['STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
# marker=Markers[model_pos['STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
# markersize=marker_s_leg, linestyle='None',
# label='Model K')
# ax.legend(handles=[RS_p, RS_i, FS, RS_p_Kv, RS_i_Kv, FS_Kv, Cb, Cb_pl, Cb_sw, STN, STN_pl, STN_sw], loc='center',
# bbox_to_anchor=pos, ncol=ncol, frameon=False)
ax.legend(handles=[Cb, RS_i, FS, RS_p, RS_i_Kv, Cb_pl, FS_Kv, RS_p_Kv, STN_pl, Cb_sw, STN_sw, STN], loc='center',
bbox_to_anchor=pos, ncol=ncol, frameon=False)
def plot_rheo_alt(ax, model='FS', color1='red', color2='dodgerblue', alteration='shift'):
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
model_names = ['RS Pyramidal','RS Inhibitory','FS',
'RS Pyramidal +$K_V1.1$','RS Inhibitory +$K_V1.1$',
'FS +$K_V1.1$','Cb stellate','Cb stellate +$K_V1.1$',
'Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$',
'STN $\Delta$$K_V1.1$']
model_name_dict = {'RS Pyramidal': 'RS Pyramidal',
'RS Inhibitory': 'RS Inhibitory',
'FS': 'FS',
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS Pyramidal +$K_V1.1$',
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS Inhibitory +$K_V1.1$',
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'FS +$K_V1.1$',
'Cb stellate': 'Cb stellate',
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb stellate +$K_V1.1$',
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb stellate $\Delta$$K_V1.1$',
'STN': 'STN',
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN +$K_V1.1$',
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN $\Delta$$K_V1.1$'}
colorslist = ['#007030', # dark green
'#F0D730', # yellow
'#C02717', # red
'#478010', # green
'#AAB71B', # lightgreen
'#F78017', # orange
'#40A787', # cyan'#
'#008797', # light blue
'#2060A7', # blue
'#D03050', # pink
'#53379B', # purple
'#873770', # magenta
]
import matplotlib.colors
colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
clr_dict = {}
for m in range(len(model_names)):
clr_dict[model_names[m]] = colors[m]
if alteration=='shift':
df = pd.read_csv('./Figures/Data/rheo_shift_ex.csv')
df = df.sort_values('alteration')
ax.set_xlabel('$\Delta$$V_{1/2}$')
elif alteration=='slope':
df = pd.read_csv('./Figures/Data/rheo_slope_ex.csv')
ax.set_xscale("log")
ax.set_xticks([0.5, 1, 2])
ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
ax.xaxis.set_minor_formatter(NullFormatter())
ax.set_xlabel('$k$/$k_{WT}$')
elif alteration=='g':
df = pd.read_csv('./Figures/Data/rheo_g_ex.csv')
ax.set_xscale("log")
ax.set_xticks([0.5, 1, 2])
ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
ax.xaxis.set_minor_formatter(NullFormatter())
ax.set_xlabel('$g$/$g_{WT}$')
for mod in model_names:
if mod == model_name_dict[model]:
ax.plot(df['alteration'], df[mod], color=clr_dict[mod], alpha=1, zorder=10, linewidth=2)
else:
ax.plot(df['alteration'], df[mod], color=clr_dict[mod], alpha=0.5, zorder=1, linewidth=1)
ax.set_ylabel('$\Delta$ Rheobase (nA)', labelpad=0)
x = df['alteration']
y = df[model_name_dict[model]]
ax.set_xlim(x.min(), x.max())
ax.set_ylim(df[model_names].min().min(), df[model_names].max().max())
# x axis color gradient
cvals = [-2., 2]
colors = ['lightgrey', 'k']
norm = plt.Normalize(min(cvals), max(cvals))
tuples = list(zip(map(norm, cvals), colors))
cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)
(xstart, xend) = ax.get_xlim()
(ystart, yend) = ax.get_ylim()
print(ystart, yend)
start = (xstart, ystart*1.0)
end = (xend, ystart*1.0)
ax = gradientaxis(ax, start, end, cmap, n=200,lw=4)
ax.spines['bottom'].set_visible(False)
# ax.set_ylim(ystart, yend)
#xlabel tick colors
# my_colors = ['lightgrey', 'grey', 'k']
# for ticklabel, tickcolor in zip(ax.get_xticklabels(), my_colors):
# ticklabel.set_color(tickcolor)
return ax
def plot_fI(ax, model='RS Pyramidal', type='shift', alt='m', color1='red', color2='dodgerblue'):
model_save_name = {'RS Pyramidal': 'RS_pyr_posp',
'RS Inhibitory': 'RS_inhib_posp',
'FS': 'FS_posp',
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS_pyr_Kv',
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS_inhib_Kv',
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'FS_Kv',
'Cb stellate': 'Cb_stellate',
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb_stellate_Kv',
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb_stellate_Kv_only',
'STN': 'STN',
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN_Kv',
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN_Kv_only'}
cvals = [-2., 2]
colors = [color1, color2]
norm = plt.Normalize(min(cvals), max(cvals))
tuples = list(zip(map(norm, cvals), colors))
cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)
colors = cmap(np.linspace(0, 1, 22))
df = pd.read_csv('./Figures/Data/Model_fI/{}_fI.csv'.format(model_save_name[model]))
df.drop(['Unnamed: 0'], axis=1)
newdf = df.loc[df.index[(df['alt'] == alt) & (df['type'] == type)], :]
newdf['mag'] = newdf['mag'].astype('float')
newdf = newdf.sort_values('mag').reset_index()
c = 0
for i in newdf.index:
ax.plot(json.loads(newdf.loc[i, 'I']), json.loads(newdf.loc[i, 'F']), color=colors[c])
c += 1
# colors2 = [colors[10, :], 'k']
# norm2 = plt.Normalize(min(cvals), max(cvals))
# tuples2 = list(zip(map(norm2, cvals), colors2))
# cmap2 = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples2)
#
# colors3 = [colors[11, :], 'lightgrey']
# norm3 = plt.Normalize(min(cvals), max(cvals))
# tuples3 = list(zip(map(norm3, cvals), colors3))
# cmap3 = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples3)
#
# start = (1.1, json.loads(newdf.loc[10, 'F'])[-1])
# end = (1.1, json.loads(newdf.loc[20, 'F'])[-1])#-json.loads(newdf.loc[20, 'F'])[-1]*0.1)
# ax = rainbowarrow(ax, start, end, cmap2, n=50, lw=1)
# ax.text(1.15, json.loads(newdf.loc[20, 'F'])[-1], '$+ \Delta V$', fontsize=4, color='k')
#
# start = (1.1, json.loads(newdf.loc[10, 'F'])[-1])
# end = (1.1, json.loads(newdf.loc[0, 'F'])[-1])#-json.loads(newdf.loc[0, 'F'])[-1]*0.1)
# ax = rainbowarrow(ax, start, end, cmap3, n=50, lw=1)
# ax.text(1.15, json.loads(newdf.loc[0, 'F'])[-1], '$- \Delta V$', fontsize=4, color='lightgrey')
ax.set_ylabel('Frequency [Hz]')
ax.set_xlabel('Current [nA]')
if model == 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':
ax.set_title("Model G", x=0.2, y=1.0)
elif model == 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':
ax.set_title("Model F", x=0.2, y=1.0)
elif model == 'Cb stellate':
ax.set_title("Model A", x=0.2, y=1.0)
else:
ax.set_title("", x=0.2, y=1.0)
# plot_rheo_alt(ax0_ex, model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k',
# alteration='shift')
# plot_rheo_alt(ax1_ex, model='Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k',
# alteration='slope')
# plot_rheo_alt(ax2_ex, model='Cb stellate', color1='lightgrey', color2='k', alteration='g')
# ax.set_title(model, x=0.2, y=1.025)
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
L = ax.get_ylim()
ax.set_ylim([0, L[1]])
return ax
#%%
boxplot_style()
color_dict = {'Cb stellate': '#40A787', # cyan'#
'RS Inhibitory': '#F0D730', # yellow
'FS': '#C02717', # red
'RS Pyramidal': '#007030', # dark green
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#AAB71B', # lightgreen
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#008797', # light blue
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#F78017', # orange
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#478010', # green
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#53379B', # purple
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#2060A7', # blue
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#873770', # magenta
'STN': '#D03050' # pink
}
# plot setup
marker_s_leg = 2
max_width = 20
pad_x = 0.85
pad_y= 0.4
pad_w = 1.1
pad_h = 0.7
fig = plt.figure()
gs = fig.add_gridspec(3, 7, wspace=1.2, hspace=1.)
ax0 = fig.add_subplot(gs[0,2:7])
ax0_ex = fig.add_subplot(gs[0,1])
ax0_fI = fig.add_subplot(gs[0,0])
ax1 = fig.add_subplot(gs[1,2:7])
ax1_ex = fig.add_subplot(gs[1,1])
ax1_fI = fig.add_subplot(gs[1,0])
ax2 = fig.add_subplot(gs[2,2:7])
ax2_ex = fig.add_subplot(gs[2,1])
ax2_fI = fig.add_subplot(gs[2,0])
line_width = 1
# plot fI curves
ax0_fI = plot_fI(ax0_fI, model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='shift', alt='s', color1='lightgrey', color2='k')
rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1 + pad_h, fill=False, lw=line_width,transform=ax0_fI.transAxes, color=color_dict['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
rec = ax0_fI.add_patch(rec)
rec.set_clip_on(False)
ax1_fI = plot_fI(ax1_fI, model='Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='slope', alt='u', color1='lightgrey', color2='k')
rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1 + pad_h, fill=False, lw=line_width,transform=ax1_fI.transAxes, color=color_dict['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
rec = ax1_fI.add_patch(rec)
rec.set_clip_on(False)
ax2_fI = plot_fI(ax2_fI, model='Cb stellate', type='g', alt='Leak', color1='lightgrey', color2='k')
rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1 + pad_h, fill=False, lw=line_width,transform=ax2_fI.transAxes, color=color_dict['Cb stellate'], alpha=1, zorder=-1)
rec = ax2_fI.add_patch(rec)
rec.set_clip_on(False)
# plot boxplots
boxplot_with_markers(ax0,max_width, alteration='shift')
boxplot_with_markers(ax1,max_width, alteration='slope')
boxplot_with_markers(ax2,max_width, alteration='g')
# plot legend
pos = (0.225, -0.9)
ncol = 6
model_legend(ax2, marker_s_leg, pos, ncol)
# plot rheo across model for example alteration
plot_rheo_alt(ax0_ex,model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k', alteration='shift')
plot_rheo_alt(ax1_ex,model='Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k',alteration='slope')
plot_rheo_alt(ax2_ex, model='Cb stellate', color1='lightgrey', color2='k', alteration='g')
# label subplots with letters
ax0_fI.text(-0.875, 1.35, string.ascii_uppercase[0], transform=ax0_fI.transAxes, size=10, weight='bold')
ax0_ex.text(-0.8, 1.35, string.ascii_uppercase[1], transform=ax0_ex.transAxes, size=10, weight='bold')
ax0.text(-0.075, 1.35, string.ascii_uppercase[2], transform=ax0.transAxes, size=10, weight='bold')
ax1_fI.text(-0.875, 1.35, string.ascii_uppercase[3], transform=ax1_fI.transAxes,size=10, weight='bold')
ax1_ex.text(-0.8, 1.35, string.ascii_uppercase[4], transform=ax1_ex.transAxes, size=10, weight='bold')
ax1.text(-0.075, 1.35, string.ascii_uppercase[5], transform=ax1.transAxes, size=10, weight='bold')
ax2_fI.text(-0.875, 1.35, string.ascii_uppercase[6], transform=ax2_fI.transAxes,size=10, weight='bold')
ax2_ex.text(-0.8, 1.35, string.ascii_uppercase[7], transform=ax2_ex.transAxes, size=10, weight='bold')
ax2.text(-0.075, 1.35, string.ascii_uppercase[8], transform=ax2.transAxes, size=10, weight='bold')
# save
fig.set_size_inches(cm2inch(20.75,12))
fig.savefig('./Figures/rheobase_correlation.pdf', dpi=fig.dpi)
# fig.savefig('./Figures/rheobase_correlation.png', dpi=fig.dpi) #bbox_inches='tight', dpi=fig.dpi # eps # pdf
plt.show()
#%%
# fig, axs = plt.subplots(1,2)
# axs[0] = plot_fI(axs[0] , model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='shift', alt='s', color1='lightgrey', color2='k')
# plt.show()
#%%
#
#
# cvals = [-2., 2]
# colors = ['lightgrey', 'k']
#
# norm = plt.Normalize(min(cvals), max(cvals))
# tuples = list(zip(map(norm, cvals), colors))
# cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)
# colors = cmap(np.linspace(0, 1, 22))
#
# colors2 = [colors[10,:], 'k']
# norm2 = plt.Normalize(min(cvals), max(cvals))
# tuples2 = list(zip(map(norm2, cvals), colors2))
# cmap2 = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples2)
#
# colors3 = [colors[11,:], 'lightgrey']
# norm3 = plt.Normalize(min(cvals), max(cvals))
# tuples3 = list(zip(map(norm3, cvals), colors3))
# cmap3 = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples3)
#
# fig, axs = plt.subplots(1,2)
# start = (0,0)
# end = (1,1)
# axs[0] = rainbowarrow(axs[0], start, end, cmap2, n=50,lw=3)
# start = (0,0)
# end = (-1,-1)
# axs[0] = rainbowarrow(axs[0], start, end, cmap3, n=50,lw=3)
# plt.show()

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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
import string
from Figures.plotstyle import sim_style
import seaborn as sns
import scipy.stats as stats
import matplotlib.lines as mlines
def cm2inch(*tupl):
inch = 2.54
if isinstance(tupl[0], tuple):
return tuple(i/inch for i in tupl[0])
else:
return tuple(i/inch for i in tupl)
def Kendall_tau(df):
tau = df.corr(method='kendall')
p = pd.DataFrame(columns=df.columns, index=df.columns)
for col in range((df.columns).shape[0]):
for col2 in range((df.columns).shape[0]):
if col != col2:
_, p.loc[df.columns[col], df.columns[col2]] = stats.kendalltau(
df[df.columns[col]], df[df.columns[col2]], nan_policy='omit')
return tau, p
def correlation_plot(ax, df='AUC', title='', cbar=False):
# cbar_ax = fig.add_axes([0.685, 0.44, .15, .01])
cbar_ax = fig.add_axes([0.685, 0.48, .15, .01])
cbar_ax.spines['left'].set_visible(False)
cbar_ax.spines['bottom'].set_visible(False)
cbar_ax.spines['right'].set_visible(False)
cbar_ax.spines['top'].set_visible(False)
cbar_ax.set_xticks([])
cbar_ax.set_yticks([])
if df == 'AUC':
df = pd.read_csv(os.path.join('./Figures/Data/sim_mut_AUC.csv'), index_col='Unnamed: 0')
elif df == 'rheo':
df = pd.read_csv(os.path.join('./Figures/Data/sim_mut_rheo.csv'), index_col='Unnamed: 0')
# array for names
cmap = sns.diverging_palette(220, 10, as_cmap=True)
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN',
'STN_Kv', 'STN_Kv_only']
model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'Cb stellate',
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'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}$']
# model_letter_names = ['Model H', 'Model E', 'Model G', 'Model A', 'Model F', 'Model J', 'Model L', 'Model I', 'Model K']
model_letter_names = ['H', 'E', 'G', 'A', 'F', 'J', 'L', 'I', 'K']
col_dict = {}
for m in range(len(models)):
col_dict[model_names[m]] = model_letter_names[m]
df.rename(columns=col_dict, inplace=True)
df = df[model_letter_names]
# calculate correlation matrix
tau, p = Kendall_tau(df)
# tau = tau.drop(columns='STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', index='RS pyramidal')
# mask to hide upper triangle of matrix
mask = np.zeros_like(tau, dtype=bool)
mask[np.triu_indices_from(mask)] = True
np.fill_diagonal(mask, False)
# models and renaming of tau
models = ['RS pyramidal', 'RS inhibitory', 'FS', 'Cb stellate',
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN',
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
model_names = ['RS pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'RS inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'Cb stellate', 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN',
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
# model_letter_names = ['Model H', 'Model E', 'Model G', 'Model A', 'Model F', 'Model J', 'Model L', 'Model I', 'Model K']
model_letter_names = ['H', 'E', 'G', 'A', 'F', 'J', 'L', 'I', 'K']
col_dict = {}
for m in range(len(models)):
col_dict[model_names[m]] = model_letter_names[m]
tau.rename(columns=col_dict, index=col_dict, inplace=True)
tau = tau[model_letter_names]
# plotting with or without colorbar
if cbar==False:
res = sns.heatmap(tau, annot=False, mask=mask, center=0, vmax=1, vmin=-1, linewidths=.5, square=True, ax=ax,
cbar=False, cmap=cmap, cbar_ax=cbar_ax, cbar_kws={"shrink": .52})
else:
res = sns.heatmap(tau, annot=False, mask=mask, center=0, vmax=1, vmin=-1, linewidths=.5, square=True, ax=ax,
cbar=True, cmap=cmap, cbar_ax=cbar_ax,
cbar_kws={"orientation": "horizontal",
"ticks": [-1,-0.5, 0, 0.5, 1]} )
cbar_ax.set_title(r'Kendall $\tau$', y=1.02, loc='center', fontsize=6)
cbar_ax.tick_params(length=3)
for tick in cbar_ax.xaxis.get_major_ticks():
tick.label.set_fontsize(6)
ax.set_title(title, fontsize=8)
ax.set_xlabel("Model")
ax.set_ylabel("Model")
def mutation_plot(ax, model='RS_pyramidal'):
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN',
'STN_Kv', 'STN_Kv_only']
model_names = ['RS pyramidal', 'RS inhibitory', 'FS',
'Cb stellate', 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN', 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
model_display_names = ['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'Cb stellate',
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN',
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
model_letter_names = ['Model H',
'Model E',
'Model G', 'Model A',
'Model F',
'Model J', 'Model L',
'Model I',
'Model K']
col_dict = {}
for m in range(len(models)):
col_dict[models[m]] = model_display_names[m]
ax_dict = {}
ax_dict['RS_pyramidal'] = (0, 0)
ax_dict['RS_inhib'] = (0, 1)
ax_dict['FS'] = (1, 0)
ax_dict['Cb_stellate'] = (2, 0)
ax_dict['Cb_stellate_Kv'] = (2, 1)
ax_dict['Cb_stellate_Kv_only'] = (3, 0)
ax_dict['STN'] = (3, 1)
ax_dict['STN_Kv'] = (4, 0)
ax_dict['STN_Kv_only'] = (4, 1)
ylim_dict = {}
ylim_dict['RS_pyramidal'] = (-0.1, 0.3)
ylim_dict['RS_inhib'] = (-0.6, 0.6)
ylim_dict['FS'] = (-0.06, 0.08)
ylim_dict['Cb_stellate'] = (-0.1, 0.4)
ylim_dict['Cb_stellate_Kv'] = (-0.1, 0.5)
ylim_dict['Cb_stellate_Kv_only'] = (-1, 0.8)
ylim_dict['STN'] = (-0.01, 0.015)
ylim_dict['STN_Kv'] = (-0.4, 0.6)
ylim_dict['STN_Kv_only'] = (-0.03, 0.3)
Marker_dict = {'Cb stellate': 'o', 'RS Inhibitory': 'o', 'FS': 'o', 'RS Pyramidal': "^",
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "^",
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "^",
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "D",
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "D",
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "D",
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "s",
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "s", 'STN': "s"}
AUC = pd.read_csv(os.path.join('./Figures/Data/sim_mut_AUC.csv'), index_col='Unnamed: 0')
rheo = pd.read_csv(os.path.join('./Figures/Data/sim_mut_rheo.csv'), index_col='Unnamed: 0')
mod = models.index(model)
mut_names = AUC.index
ax.plot(rheo.loc[mut_names, model_names[mod]]*1000, AUC.loc[mut_names, model_names[mod]]*100, linestyle='',
markeredgecolor='grey', markerfacecolor='grey', marker=Marker_dict[model_display_names[mod]],
markersize=2)
ax.plot(rheo.loc['wt', model_names[mod]], AUC.loc['wt', model_names[mod]]*100, 'sk')
mut_col = sns.color_palette("pastel")
ax.plot(rheo.loc['V174F', model_names[mod]]*1000, AUC.loc['V174F', model_names[mod]]*100, linestyle='',
markeredgecolor=mut_col[0], markerfacecolor=mut_col[0], marker=Marker_dict[model_display_names[mod]],markersize=4)
ax.plot(rheo.loc['F414C', model_names[mod]]*1000, AUC.loc['F414C', model_names[mod]]*100, linestyle='',
markeredgecolor=mut_col[1], markerfacecolor=mut_col[1], marker=Marker_dict[model_display_names[mod]],markersize=4)
ax.plot(rheo.loc['E283K', model_names[mod]]*1000, AUC.loc['E283K', model_names[mod]]*100, linestyle='',
markeredgecolor=mut_col[2], markerfacecolor=mut_col[2], marker=Marker_dict[model_display_names[mod]],markersize=4)
ax.plot(rheo.loc['V404I', model_names[mod]]*1000, AUC.loc['V404I', model_names[mod]]*100, linestyle='',
markeredgecolor=mut_col[3], markerfacecolor=mut_col[5], marker=Marker_dict[model_display_names[mod]],markersize=4)
ax.set_title(model_letter_names[mod], pad=14)
ax.set_xlabel('$\Delta$Rheobase [pA]')
ax.set_ylabel('Normalized $\Delta$AUC (%)')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# ax.ticklabel_format(axis="y", style="sci", scilimits=(0, 0),useMathText=True)
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
ax.hlines(0, xmin, xmax, colors='lightgrey', linestyles='--')
ax.vlines(0, ymin,ymax, colors='lightgrey', linestyles='--')
return ax
def mutation_legend(ax, marker_s_leg, pos, ncol):
colors = sns.color_palette("pastel")
Markers = ["o", "o", "o", "o"]
V174F = mlines.Line2D([], [], color=colors[0], marker=Markers[0], markersize=marker_s_leg, linestyle='None',
label='V174F')
F414C = mlines.Line2D([], [], color=colors[1], marker=Markers[1], markersize=marker_s_leg, linestyle='None',
label='F414C')
E283K = mlines.Line2D([], [], color=colors[2], marker=Markers[2], markersize=marker_s_leg, linestyle='None', label='E283K')
V404I = mlines.Line2D([], [], color=colors[5], marker=Markers[3], markersize=marker_s_leg, linestyle='None',
label='V404I')
WT = mlines.Line2D([], [], color='k', marker='s', markersize=marker_s_leg+2, linestyle='None', label='Wild type')
ax.legend(handles=[WT, V174F, F414C, E283K, V404I], loc='center', bbox_to_anchor=pos, ncol=ncol, frameon=False)
sim_style()
# plot setup
fig = plt.figure()
gs0 = fig.add_gridspec(1, 6, wspace=-0.2)
gsl = gs0[0:3].subgridspec(3, 3, wspace=0.9, hspace=0.8)
gsr = gs0[4:6].subgridspec(7, 1, wspace=0.6, hspace=0.8)
ax00 = fig.add_subplot(gsl[0,0])
ax01 = fig.add_subplot(gsl[0,1])
ax02 = fig.add_subplot(gsl[0,2])
ax10 = fig.add_subplot(gsl[1,0])
ax11 = fig.add_subplot(gsl[1,1])
ax12 = fig.add_subplot(gsl[1,2])
ax20 = fig.add_subplot(gsl[2,0])
ax21 = fig.add_subplot(gsl[2,1])
ax22 = fig.add_subplot(gsl[2,2])
axr0 = fig.add_subplot(gsr[0:3,0])
axr1 = fig.add_subplot(gsr[4:,0])
# plot mutations in each model
ax00 = mutation_plot(ax00, model='RS_pyramidal')
ax01 = mutation_plot(ax01, model='RS_inhib')
ax02 = mutation_plot(ax02, model='FS')
ax10 = mutation_plot(ax10, model='Cb_stellate')
ax11 = mutation_plot(ax11, model='Cb_stellate_Kv')
ax12 = mutation_plot(ax12, model='Cb_stellate_Kv_only')
ax20 = mutation_plot(ax20, model='STN')
ax21 = mutation_plot(ax21, model='STN_Kv')
ax22 = mutation_plot(ax22, model='STN_Kv_only')
marker_s_leg = 4
pos = (0.425, -0.7)
ncol = 5
mutation_legend(ax21, marker_s_leg, pos, ncol)
# plot correlation matrices
correlation_plot(axr1,df = 'AUC', title='Normalized $\Delta$AUC', cbar=False)
correlation_plot(axr0,df = 'rheo', title='$\Delta$Rheobase', cbar=True)
# add subplot labels
axs = [ax00, ax01,ax02, ax10, ax11, ax12, ax20, ax21, ax22]
j=0
for i in range(0,9):
# axs[i].text(-0.48, 1.175, string.ascii_uppercase[i], transform=axs[i].transAxes, size=10, weight='bold')
axs[i].text(-0.625, 1.25, string.ascii_uppercase[i], transform=axs[i].transAxes, size=10, weight='bold')
j +=1
axr0.text(-0.77, 1.1, string.ascii_uppercase[j], transform=axr0.transAxes, size=10, weight='bold')
axr1.text(-0.77, 1.1, string.ascii_uppercase[j+1], transform=axr1.transAxes, size=10, weight='bold')
# save
fig.set_size_inches(cm2inch(22.2,15))
fig.savefig('./Figures/simulation_model_comparison.pdf', dpi=fig.dpi) #eps
# fig.savefig('./Figures/simulation_model_comparison.png', dpi=fig.dpi) #eps
plt.show()

14
manuscript.tex
View File

@ -190,8 +190,7 @@ However, the effect of a given channelopathy on the firing behavior of different
For instance, altering relative amplitudes of ionic currents can dramatically influence the firing behavior and dynamics of neurons \citep{rutecki_neuronal_1992, pospischil_minimal_2008,Kispersky2012, golowasch_failure_2002, barreiro_-current_2012, Noebels2017, Layer2021}, however other properties of ionic currents impact neuronal firing as well. Cell-type specific effects one firing can occur for instance increases inhibitory interneuron but not pyramidal neuron firing with R1648H mutation in \textit{SCN1A} \citep{Hedrich14874}. In extreme cases, a mutation can have opposite effects on different neuron types. For example, the R1627H \textit{SCN8A} mutation is associated which increased firing in interneurons, but decreases pyramidal neuron excitability \citep{makinson_scn1a_2016}. For instance, altering relative amplitudes of ionic currents can dramatically influence the firing behavior and dynamics of neurons \citep{rutecki_neuronal_1992, pospischil_minimal_2008,Kispersky2012, golowasch_failure_2002, barreiro_-current_2012, Noebels2017, Layer2021}, however other properties of ionic currents impact neuronal firing as well. Cell-type specific effects one firing can occur for instance increases inhibitory interneuron but not pyramidal neuron firing with R1648H mutation in \textit{SCN1A} \citep{Hedrich14874}. In extreme cases, a mutation can have opposite effects on different neuron types. For example, the R1627H \textit{SCN8A} mutation is associated which increased firing in interneurons, but decreases pyramidal neuron excitability \citep{makinson_scn1a_2016}.
\textcolor{red}{Despite this evidence of cell-type specific effects of ion channel mutations on firing, the dependence of firing outcomes of ion channel mutations is generally not known. Cell-type specificity is likely vital for successful precision medicine treatment approaches. For example, Dravet syndrome was identified as the consquence of LOF mutations in \textit{SCN1A} \citep{Claes2001,Fujiwara2003,Ohmori2002}, however limited succes in treatment of Dravet syndrome persisted \citep{Claes2001,Oguni2001}. Once it became evident that only inhibitory interneurons and not pyramidal neurons had altered excitability as a result of LOF \textit{SCN1A} mutations alternative approaches, based on this understanding such as gene therapy, began to show promise \citep{Colasante2020, Yu2006}. Due to the high clinical relevance of understanding cell-type dependent effects of channelopathies, we use computationaly modelling approaches to assess the impacts of altered ionic current properties on firing behavior, bridging the gap between changes in the biophysical properties induced by mutations, firing and clinical symptoms. Conductance-based neuronal models enable insight into the effects of ion channel mutations with specific effects of the resulting ionic current as well as enabling \textit{in silico} assessment of the relative effects of changes in biophysical properties of ionic currents on neuronal firing. Furthermore, modelling approaches enable predictions of the effects of specific mutation and drug induced biophysical property changes \citep{Layer2021,Liu2019,johannesen_genotype-phenotype_2021, lauxmann_therapeutic_2021}. Despite this evidence of cell-type specific effects of ion channel mutations on firing, the dependence of firing outcomes of ion channel mutations is generally not known. Cell-type specificity is likely vital for successful precision medicine treatment approaches. For example, Dravet syndrome was identified as the consquence of LOF mutations in \textit{SCN1A} \citep{Claes2001,Fujiwara2003,Ohmori2002}, however limited succes in treatment of Dravet syndrome persisted \citep{Claes2001,Oguni2001}. Once it became evident that only inhibitory interneurons and not pyramidal neurons had altered excitability as a result of LOF \textit{SCN1A} mutations alternative approaches, based on this understanding such as gene therapy, began to show promise \citep{Colasante2020, Yu2006}. Due to the high clinical relevance of understanding cell-type dependent effects of channelopathies, we use computationaly modelling approaches to assess the impacts of altered ionic current properties on firing behavior, bridging the gap between changes in the biophysical properties induced by mutations, firing and clinical symptoms. Conductance-based neuronal models enable insight into the effects of ion channel mutations with specific effects of the resulting ionic current as well as enabling \textit{in silico} assessment of the relative effects of changes in biophysical properties of ionic currents on neuronal firing. Furthermore, modelling approaches enable predictions of the effects of specific mutation and drug induced biophysical property changes \citep{Layer2021,Liu2019,johannesen_genotype-phenotype_2021, lauxmann_therapeutic_2021}.
}
%Computational modelling approaches can be used to assess the impacts of altered ionic current properties on firing behavior, bridging the gap between changes in the biophysical properties induced by mutations, firing and clinical symptoms. Conductance-based neuronal models enable insight into the effects of ion channel mutations with specific effects of the resulting ionic current as well as enabling \textit{in silico} assessment of the relative effects of changes in biophysical properties of ionic currents on neuronal firing. Furthermore, modelling approaches enable predictions of the effects of specific mutation and drug induced biophysical property changes \citep{Layer2021,Liu2019,johannesen_genotype-phenotype_2021, lauxmann_therapeutic_2021}. %Computational modelling approaches can be used to assess the impacts of altered ionic current properties on firing behavior, bridging the gap between changes in the biophysical properties induced by mutations, firing and clinical symptoms. Conductance-based neuronal models enable insight into the effects of ion channel mutations with specific effects of the resulting ionic current as well as enabling \textit{in silico} assessment of the relative effects of changes in biophysical properties of ionic currents on neuronal firing. Furthermore, modelling approaches enable predictions of the effects of specific mutation and drug induced biophysical property changes \citep{Layer2021,Liu2019,johannesen_genotype-phenotype_2021, lauxmann_therapeutic_2021}.
@ -311,7 +310,8 @@ Qualitative differences can be found, for example, when increasing the maximal c
\centering \centering
\includegraphics[width=\linewidth]{Figures/AUC_correlation.pdf} \includegraphics[width=\linewidth]{Figures/AUC_correlation.pdf}
\linespread{1.}\selectfont \linespread{1.}\selectfont
\caption[]{Effects of altered channel kinetics on AUC in various neuron models. The fI curves corresponding to shifts in FS \(+\)\Kv model delayed rectifier K half activation \(V_{1/2}\) (A), changes \Kv activation slope factor \(k\) in the FS \(+\)\Kv model (D), and changes in maximal conductance of delayed rectifier K current in the STN \(+\)\Kv model (G) are shown. The fI curves from the smallest (grey) to the largest (black) alterations are seen for (A,D, and G) in accordance to the greyscale of the x-axis in B, E, and H. The \ndAUC of fI curves is plotted against delayed rectifier K half activation potential (\(\Delta V_{1/2}\); D), \Kv activation slope factor \(k\) (k/\(\textrm{k}_{WT}\); E) and maximal conductance \(g\) of the delayed rectifier K current (g/\(\textrm{g}_{WT}\); H) for all models (thin lines) with relationships from the fI curve examples (A, D, G respectively) highlighted by thick lines with colors corresponding to the box highlighting each set of fI curves. The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in half maximal potential \(V_{1/2}\) and \ndAUC (C), slope factor k and \ndAUC (F) as well as maximal current conductances and \ndAUC (I) for each model and current property is computed. The relationships between \(\Delta V_{1/2}\), k/\(\textrm{k}_{WT}\), and g/\(\textrm{g}_{WT}\) and \ndAUC for the Kendall rank correlations highlighted in the black boxes are depicted in (B), (E) and (H) respectively. } % \caption[]{Effects of altered channel kinetics on AUC in various neuron models. The fI curves corresponding to shifts in FS \(+\)\Kv model delayed rectifier K half activation \(V_{1/2}\) (A), changes \Kv activation slope factor \(k\) in the FS \(+\)\Kv model (D), and changes in maximal conductance of delayed rectifier K current in the STN \(+\)\Kv model (G) are shown. The fI curves from the smallest (grey) to the largest (black) alterations are seen for (A,D, and G) in accordance to the greyscale of the x-axis in B, E, and H. The \ndAUC of fI curves is plotted against delayed rectifier K half activation potential (\(\Delta V_{1/2}\); D), \Kv activation slope factor \(k\) (k/\(\textrm{k}_{WT}\); E) and maximal conductance \(g\) of the delayed rectifier K current (g/\(\textrm{g}_{WT}\); H) for all models (thin lines) with relationships from the fI curve examples (A, D, G respectively) highlighted by thick lines with colors corresponding to the box highlighting each set of fI curves. The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in half maximal potential \(V_{1/2}\) and \ndAUC (C), slope factor k and \ndAUC (F) as well as maximal current conductances and \ndAUC (I) for each model and current property is computed. The relationships between \(\Delta V_{1/2}\), k/\(\textrm{k}_{WT}\), and g/\(\textrm{g}_{WT}\) and \ndAUC for the Kendall rank correlations highlighted in the black boxes are depicted in (B), (E) and (H) respectively. }
\caption[]{Effects of altered channel kinetics on AUC in various neuron models. The fI curves corresponding to shifts in model G delayed rectifier K half activation \(V_{1/2}\) (A), changes \Kv activation slope factor \(k\) in model G (D), and changes in maximal conductance of delayed rectifier K current in the model I (G) are shown. The fI curves from the smallest (grey) to the largest (black) alterations are seen for (A,D, and G) in accordance to the greyscale of the x-axis in B, E, and H. The \ndAUC of fI curves is plotted against delayed rectifier K half activation potential (\(\Delta V_{1/2}\); D), \Kv activation slope factor \(k\) (k/\(\textrm{k}_{WT}\); E) and maximal conductance \(g\) of the delayed rectifier K current (g/\(\textrm{g}_{WT}\); H) for all models (thin lines) with relationships from the fI curve examples (A, D, G respectively) highlighted by thick lines with colors corresponding to the box highlighting each set of fI curves. The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in half maximal potential \(V_{1/2}\) and \ndAUC (C), slope factor k and \ndAUC (F) as well as maximal current conductances and \ndAUC (I) for each model and current property is computed. The relationships between \(\Delta V_{1/2}\), k/\(\textrm{k}_{WT}\), and g/\(\textrm{g}_{WT}\) and \ndAUC for the Kendall rank correlations highlighted in the black boxes are depicted in (B), (E) and (H) respectively. }
\label{fig:AUC_correlation} \label{fig:AUC_correlation}
\end{figure} \end{figure}
@ -321,14 +321,15 @@ Changes in gating half activation potential \(V_{1/2}\) and slope factor \(k\) a
%Although changes in half maximal potential \(V_{1/2}\) and slope factor \(k\) generally correlated with rheobase similarly across model there were some exceptions. Changing the slope factor of Na-current inactivation, \Kv-current inactivation, and A-current activation affected the rheobase both with positive and negative correlations in different models \textcolor{red}{\noteuh{Würde diese hier noch mal benennen, damit es klar wird. }}\notenk{Ich mache das ungern, weil ich für jedes (Na-current inactivation, \Kv-current inactivation, and A-current activation) 2 Liste habe (+ und - rheobase Aenderungen} (\Cref{fig:rheobase_correlation}~F). Departures from monotonic relationships also occurred in some models as a result of K-current activation \(V_{1/2}\) and slope factor \(k\), \Kv-current inactivation slope factor \(k\), and A-current activation slope factor \(k\) in some models \textcolor{red}{\noteuh{Auch hier die unterschiedlcihen betroffenen cell type models benennen, einfach in Klammer dahinter.}}\notenk{Hier mache ich das auch ungern, für ähnlichen Gründen}. Thus, identical changes in current gating properties such as the half maximal potential \(V_{1/2}\) or slope factor \(k\) can have differing effects on firing depending on the model in which they occur. %Although changes in half maximal potential \(V_{1/2}\) and slope factor \(k\) generally correlated with rheobase similarly across model there were some exceptions. Changing the slope factor of Na-current inactivation, \Kv-current inactivation, and A-current activation affected the rheobase both with positive and negative correlations in different models \textcolor{red}{\noteuh{Würde diese hier noch mal benennen, damit es klar wird. }}\notenk{Ich mache das ungern, weil ich für jedes (Na-current inactivation, \Kv-current inactivation, and A-current activation) 2 Liste habe (+ und - rheobase Aenderungen} (\Cref{fig:rheobase_correlation}~F). Departures from monotonic relationships also occurred in some models as a result of K-current activation \(V_{1/2}\) and slope factor \(k\), \Kv-current inactivation slope factor \(k\), and A-current activation slope factor \(k\) in some models \textcolor{red}{\noteuh{Auch hier die unterschiedlcihen betroffenen cell type models benennen, einfach in Klammer dahinter.}}\notenk{Hier mache ich das auch ungern, für ähnlichen Gründen}. Thus, identical changes in current gating properties such as the half maximal potential \(V_{1/2}\) or slope factor \(k\) can have differing effects on firing depending on the model in which they occur.
Although changes in half maximal potential \(V_{1/2}\) and slope factor \(k\) generally correlated with rheobase similarly across model there were some exceptions. Rheobase was affected with both with positive and negative correlations in different models as a result of changing slope factor of \textcolor{red}{Na-current inactivation (positive: models A-H and J; negative: models I, K and L), \Kv-current inactivation (positive: models I and K; negative: models E-G, J, H), and A-current activation (positive: models A,F and L; negative: model I)} (\Cref{fig:rheobase_correlation}~F). Departures from monotonic relationships also occurred in some models as a result of \textcolor{red}{K-current activation \(V_{1/2}\) (e.g. model J) and slope factor \(k\) (models F and G), \Kv-current inactivation slope factor \(k\) (model K), and A-current activation slope factor \(k\) (model L) }. Thus, identical changes in current gating properties such as the half maximal potential \(V_{1/2}\) or slope factor \(k\) can have differing effects on firing depending on the model in which they occur. Although changes in half maximal potential \(V_{1/2}\) and slope factor \(k\) generally correlated with rheobase similarly across model there were some exceptions. Rheobase was affected with both with positive and negative correlations in different models as a result of changing slope factor of Na-current inactivation (positive: models A-H and J; negative: models I, K and L), \Kv-current inactivation (positive: models I and K; negative: models E-G, J, H), and A-current activation (positive: models A,F and L; negative: model I; \Cref{fig:rheobase_correlation}~F). Departures from monotonic relationships also occurred in some models as a result of K-current activation \(V_{1/2}\) (e.g. model J) and slope factor \(k\) (models F and G), \Kv-current inactivation slope factor \(k\) (model K), and A-current activation slope factor \(k\) (model L). Thus, identical changes in current gating properties such as the half maximal potential \(V_{1/2}\) or slope factor \(k\) can have differing effects on firing depending on the model in which they occur.
\begin{figure}[tp] \begin{figure}[tp]
\centering \centering
\includegraphics[width=\linewidth]{Figures/rheobase_correlation.pdf} \includegraphics[width=\linewidth]{Figures/rheobase_correlation.pdf}
\linespread{1.}\selectfont \linespread{1.}\selectfont
\caption[]{Effects of altered channel kinetics on rheobase. The fI curves corresponding to shifts in FS \(+\)\Kv model \Kv activation \(V_{1/2}\) (A), changes \Kv inactivation slope factor \(k\) in the Cb stellate \(+\)\Kv model (D), and changes in maximal conductance of the leak current in the Cb stellate model (G) are shown. The fI curves from the smallest (grey) to the largest (black) alterations are seen for (A,D, and G) in accordance to the greyscale of the x-axis in B, E, and H. The \drheo of fI curves is plotted against \Kv half activation potential (\(\Delta V_{1/2}\); B), \Kv inactivation slope factor \(k\) (k/\(\textrm{k}_{WT}\); E) and maximal conductance \(g\) of the leak current (g/\(\textrm{g}_{WT}\); H) for all models (thin lines) with relationships from the fI curve examples (A, D, G respectively) highlighted by thick lines with colors corresponding to the box highlighting each set of fI curves. The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in half maximal potential \(V_{1/2}\) and \drheo (C), slope factor k and \drheo (F) as well as maximal current conductances and \drheo (I) for each model and current property is computed. The relationships between \(\Delta V_{1/2}\), k/\(\textrm{k}_{WT}\), and g/\(\textrm{g}_{WT}\) and \drheo for the Kendall rank correlations highlighted in the black boxes are depicted in (B), (E) and (H) respectively.} % \caption[]{Effects of altered channel kinetics on rheobase. The fI curves corresponding to shifts in FS \(+\)\Kv model \Kv activation \(V_{1/2}\) (A), changes \Kv inactivation slope factor \(k\) in the Cb stellate \(+\)\Kv model (D), and changes in maximal conductance of the leak current in the Cb stellate model (G) are shown. The fI curves from the smallest (grey) to the largest (black) alterations are seen for (A,D, and G) in accordance to the greyscale of the x-axis in B, E, and H. The \drheo of fI curves is plotted against \Kv half activation potential (\(\Delta V_{1/2}\); B), \Kv inactivation slope factor \(k\) (k/\(\textrm{k}_{WT}\); E) and maximal conductance \(g\) of the leak current (g/\(\textrm{g}_{WT}\); H) for all models (thin lines) with relationships from the fI curve examples (A, D, G respectively) highlighted by thick lines with colors corresponding to the box highlighting each set of fI curves. The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in half maximal potential \(V_{1/2}\) and \drheo (C), slope factor k and \drheo (F) as well as maximal current conductances and \drheo (I) for each model and current property is computed. The relationships between \(\Delta V_{1/2}\), k/\(\textrm{k}_{WT}\), and g/\(\textrm{g}_{WT}\) and \drheo for the Kendall rank correlations highlighted in the black boxes are depicted in (B), (E) and (H) respectively.}
\caption[]{Effects of altered channel kinetics on rheobase. The fI curves corresponding to shifts in model G \Kv activation \(V_{1/2}\) (A), changes \Kv inactivation slope factor \(k\) in model F (D), and changes in maximal conductance of the leak current in model A (G) are shown. The fI curves from the smallest (grey) to the largest (black) alterations are seen for (A,D, and G) in accordance to the greyscale of the x-axis in B, E, and H. The \drheo of fI curves is plotted against \Kv half activation potential (\(\Delta V_{1/2}\); B), \Kv inactivation slope factor \(k\) (k/\(\textrm{k}_{WT}\); E) and maximal conductance \(g\) of the leak current (g/\(\textrm{g}_{WT}\); H) for all models (thin lines) with relationships from the fI curve examples (A, D, G respectively) highlighted by thick lines with colors corresponding to the box highlighting each set of fI curves. The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in half maximal potential \(V_{1/2}\) and \drheo (C), slope factor k and \drheo (F) as well as maximal current conductances and \drheo (I) for each model and current property is computed. The relationships between \(\Delta V_{1/2}\), k/\(\textrm{k}_{WT}\), and g/\(\textrm{g}_{WT}\) and \drheo for the Kendall rank correlations highlighted in the black boxes are depicted in (B), (E) and (H) respectively.}
\label{fig:rheobase_correlation} \label{fig:rheobase_correlation}
\end{figure} \end{figure}
@ -339,7 +340,8 @@ Mutations in \textit{KCNA1} are associated with episodic ataxia type~1 (EA1) and
\centering \centering
\includegraphics[width=\linewidth]{Figures/simulation_model_comparison.pdf} \includegraphics[width=\linewidth]{Figures/simulation_model_comparison.pdf}
\linespread{1.}\selectfont \linespread{1.}\selectfont
\caption[]{Effects of episodic ataxia type~1 associated \textit{KCNA1} mutations on firing. Effects of \textit{KCNA1} mutations on AUC (percent change in normalized \(\Delta\)AUC) and rheobase (\(\Delta\)Rheobase) compared to wild type for RS pyramidal +\Kv (A), RS inhibitory +\Kv (B), FS +\Kv (C), Cb stellate (D), Cb stellate +\Kv (E), Cb stellate \(\Delta\)\Kv (F), STN (G), STN +\Kv (H) and STN \(\Delta\)\Kv (I) models. All \textit{KCNA1} Mutations are marked in grey with the V174F, F414C, E283K, and V404I \textit{KCNA1} mutations highlighted in color for each model. Pairwise Kendall rank correlation coefficients (Kendall \(\tau\)) between the effects of \textit{KCNA1} mutations on rheobase and on AUC are shown in J and K respectively. Marker shape is indicative of model/firing type, and grey dashed lines denote the quadrants of firing characterization (see \Cref{fig:firing_characterization}).} % \caption[]{Effects of episodic ataxia type~1 associated \textit{KCNA1} mutations on firing. Effects of \textit{KCNA1} mutations on AUC (percent change in normalized \(\Delta\)AUC) and rheobase (\(\Delta\)Rheobase) compared to wild type for RS pyramidal +\Kv (A), RS inhibitory +\Kv (B), FS +\Kv (C), Cb stellate (D), Cb stellate +\Kv (E), Cb stellate \(\Delta\)\Kv (F), STN (G), STN +\Kv (H) and STN \(\Delta\)\Kv (I) models. All \textit{KCNA1} Mutations are marked in grey with the V174F, F414C, E283K, and V404I \textit{KCNA1} mutations highlighted in color for each model. Pairwise Kendall rank correlation coefficients (Kendall \(\tau\)) between the effects of \textit{KCNA1} mutations on rheobase and on AUC are shown in J and K respectively. Marker shape is indicative of model/firing type, and grey dashed lines denote the quadrants of firing characterization (see \Cref{fig:firing_characterization}).}
\caption[]{Effects of episodic ataxia type~1 associated \textit{KCNA1} mutations on firing. Effects of \textit{KCNA1} mutations on AUC (percent change in normalized \(\Delta\)AUC) and rheobase (\(\Delta\)Rheobase) compared to wild type for model H (A), model E (B), model G (C), model A (D), model F (E), model J (F), model L (G), model I (H) and model K (I). All \textit{KCNA1} Mutations are marked in grey with the V174F, F414C, E283K, and V404I \textit{KCNA1} mutations highlighted in color for each model. Pairwise Kendall rank correlation coefficients (Kendall \(\tau\)) between the effects of \textit{KCNA1} mutations on rheobase and on AUC are shown in J and K respectively. Marker shape is indicative of model/firing type, and grey dashed lines denote the quadrants of firing characterization (see \Cref{fig:firing_characterization}).}
\label{fig:simulation_model_comparision} \label{fig:simulation_model_comparision}
\end{figure} \end{figure}