In [1]:
# PyABC imports
from pyabc import (ABCSMC, Distribution, RV,
History, MedianEpsilon)
from pyabc.populationstrategy import ConstantPopulationSize, AdaptivePopulationSize
from pyabc.epsilon import MedianEpsilon
from pyabc.sampler import MulticoreEvalParallelSampler
In [2]:
# Custom imports
from ionchannelABC import (ion_channel_sum_stats_calculator,
IonChannelAcceptor,
IonChannelDistance,
EfficientMultivariateNormalTransition,
calculate_parameter_sensitivity,
plot_parameter_sensitivity,
plot_regression_fit,
plot_distance_weights,
plot_sim_results,
plot_parameters_kde)
In [3]:
# Other necessary imports
import numpy as np
import subprocess
import pandas as pd
import io
import os
import tempfile
In [4]:
# Plotting imports
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
%config Inline.Backend.figure_format = 'retina'
In [5]:
from channels.ina_generic import ina as model
model.sample({})
Out[5]:
In [6]:
measurements = model.get_experiment_data()
obs = measurements.to_dict()['y']
exp = measurements.to_dict()['exp']
errs = measurements.to_dict()['errs']
In [7]:
limits = dict(g_Na=(0, 10),
Vhalf_m=(-100,0),
k_m=(0,20),
c_bm=(0,0.1),
c_am=(0,1),
Vmax_m=(-100,0),
sigma_m=(0,100),
Vhalf_h=(-100,0),
k_h=(-20,0),
c_bh=(0,0.1),
c_ah=(0,50),
Vmax_h=(-100,0),
sigma_h=(0,100),
c_bj=(0,10),
c_aj=(50,100),
Vmax_j=(-100,0),
sigma_j=(0,100))
prior = Distribution(**{key: RV("uniform", a, b - a)
for key, (a,b) in limits.items()})
In [8]:
parameters = ['ina.'+k for k in limits.keys()]
In [9]:
distance_fn=IonChannelDistance(
obs=obs,
exp_map=exp,
err_bars=errs,
err_th=0.1)
In [10]:
sns.set_context('talk')
g = plot_distance_weights(model, distance_fn)
g.savefig('results/ina-generic/dist_weights.pdf')
In [14]:
fitted, regression_fit, r2 = calculate_parameter_sensitivity(
model,
parameters,
distance_fn,
sigma=0.05,
n_samples=1000)
In [15]:
sns.set_context('talk')
grid1 = plot_parameter_sensitivity(fitted, plot_cutoff=0.05)
In [16]:
grid2 = plot_regression_fit(regression_fit, r2)
In [17]:
grid1.savefig('results/ina-generic/sensitivity.pdf')
grid2.savefig('results/ina-generic/sensitivity_fit.pdf')
In [32]:
# Finding insensitive parameters
cutoff = 0.05
fitted_pivot = fitted.pivot(index='param',columns='exp')
insensitive_params = fitted_pivot[(abs(fitted_pivot['beta'][0])<cutoff) & (abs(fitted_pivot['beta'][1])<cutoff) &
(abs(fitted_pivot['beta'][2])<cutoff) & (abs(fitted_pivot['beta'][3])<cutoff) &
(abs(fitted_pivot['beta'][4])<cutoff)].index.values
In [42]:
insensitive_params
Out[42]:
In [43]:
insensitive_params = np.delete(insensitive_params,5)
In [91]:
insensitive_params
Out[91]:
In [92]:
insensitive_limits = dict((k, limits[k[4:]]) for k in insensitive_params)
insensitive_prior = Distribution(**{key: RV("uniform", a, b - a)
for key, (a,b) in insensitive_limits.items()})
In [93]:
# Generate random samples for insensitive parameters
def generate_sample(insensitive_prior, n):
samples = [dict() for i in range(n)]
for i in range(n):
parameters = insensitive_prior.rvs()
sample = {key: value for key, value in parameters.items()}
samples[i].update(sample)
return samples
In [94]:
samples = generate_sample(insensitive_prior, 1000)
In [95]:
model.add_external_par_samples(samples)
In [96]:
limits = dict((k, limits[k]) for k in limits if 'ina.'+k not in insensitive_params)
In [97]:
limits
Out[97]:
In [8]:
prior = Distribution(**{key: RV("uniform", a, b - a)
for key, (a,b) in limits.items()})
In [9]:
db_path = ('sqlite:///' +
os.path.join(tempfile.gettempdir(), "hl-1_ina-generic3000.db"))
print(db_path)
In [10]:
# Let's log all the sh!t
import logging
logging.basicConfig()
abc_logger = logging.getLogger('ABC')
abc_logger.setLevel(logging.DEBUG)
eps_logger = logging.getLogger('Epsilon')
eps_logger.setLevel(logging.DEBUG)
cv_logger = logging.getLogger('CV Estimation')
cv_logger.setLevel(logging.DEBUG)
In [11]:
from pyabc.populationstrategy import ConstantPopulationSize
In [12]:
abc = ABCSMC(models=model,
parameter_priors=prior,
distance_function=IonChannelDistance(
obs=obs,
exp_map=exp,
err_bars=errs,
err_th=0.1),
population_size=ConstantPopulationSize(3000),
#population_size=AdaptivePopulationSize(
# start_nr_particles=1000,
# mean_cv=0.2,
# max_population_size=1000,
# min_population_size=100),
summary_statistics=ion_channel_sum_stats_calculator,
transitions=EfficientMultivariateNormalTransition(scaling=0.8),
eps=MedianEpsilon(),
sampler=MulticoreEvalParallelSampler(n_procs=12),
acceptor=IonChannelAcceptor())
In [13]:
abc_id = abc.new(db_path, obs)
In [ ]:
history = abc.run(minimum_epsilon=0.05, max_nr_populations=30, min_acceptance_rate=0.001)
In [ ]:
history = abc.run(minimum_epsilon=0.05, max_nr_populations=30, min_acceptance_rate=0.001)
In [37]:
db_path = 'sqlite:////scratch/cph211/tmp/hl-1_ina-generic3000.db'
history = History(db_path)
history.all_runs()
Out[37]:
In [38]:
history.id = 1
In [47]:
sns.set_context('talk')
evolution = history.get_all_populations()
grid = sns.relplot(x='t', y='epsilon', size='samples', data=evolution[evolution.t>=2])
grid.savefig('results/ina-generic/eps_evolution.pdf')
In [48]:
df, w = history.get_distribution(m=0)
In [49]:
g = plot_parameters_kde(df, w, limits, aspect=12, height=0.6)
In [50]:
g.savefig('results/ina-generic/parameters_kde.pdf')
In [51]:
# Generate parameter samples
n_samples = 100
df, w = history.get_distribution(m=0)
th_samples = df.sample(n=n_samples, weights=w, replace=True).to_dict(orient='records')
In [52]:
# Generate sim results samples
samples = pd.DataFrame({})
for i, th in enumerate(th_samples):
output = model.sample(pars=th, n_x=50)
output['sample'] = i
output['distribution'] = 'post'
samples = samples.append(output, ignore_index=True)
In [53]:
from ionchannelABC import plot_sim_results
sns.set_context('talk')
g = plot_sim_results(samples, obs=measurements)
# Set axis labels
xlabels = ["voltage, mV", "voltage, mV", "voltage, mV", "time, ms", "time, ms"]
ylabels = ["current density, pA/pF", "activation", "inactivation", "recovery", "normalised current trace"]
for ax, xl in zip(g.axes.flatten(), xlabels):
ax.set_xlabel(xl)
for ax, yl in zip(g.axes.flatten(), ylabels):
ax.set_ylabel(yl)
In [54]:
g.savefig('results/ina-generic/ina_sim_results.pdf')
In [55]:
def plot_sim_results_all(samples: pd.DataFrame):
with sns.color_palette("gray"):
grid = sns.relplot(x='x', y='y',
col='exp',
units='sample',
kind='line',
data=samples,
estimator=None, lw=0.5,
alpha=0.5,
#estimator=np.median,
facet_kws={'sharex': 'col',
'sharey': 'col'})
return grid
In [56]:
grid2 = plot_sim_results_all(samples)
In [57]:
grid2.savefig('results/ina-generic/ina_sim_results_all.pdf')
In [113]:
# Require discrete samples for exact measurements at -20mV
discrete_samples = pd.DataFrame({})
for i, th in enumerate(th_samples):
output = model.sample(pars=th)
output['sample'] = i
output['distribution'] = 'post'
discrete_samples = discrete_samples.append(output, ignore_index=True)
In [114]:
# Amplitude at -20 mV
grouped = discrete_samples[discrete_samples['exp']==0].groupby('sample')
def get_amplitude(group):
return group.loc[group.x==-20]['y']
print(grouped.apply(get_amplitude).mean())
print(grouped.apply(get_amplitude).std())
In [115]:
import scipy.stats as st
peak_current = discrete_samples[discrete_samples['exp']==0].groupby('sample').apply(get_amplitude).tolist()
rv = st.rv_discrete(values=(peak_current, [1/len(peak_current),]*len(peak_current)))
print("median: {}".format(rv.median()))
print("95% CI: {}".format(rv.interval(0.95)))
In [116]:
# Voltage and slope factor at half-activation
from scipy.optimize import curve_fit
grouped = samples[samples['exp']==1].groupby('sample')
def fit_boltzmann(group):
def boltzmann(V, Vhalf, Shalf):
return 1/(1+np.exp((Vhalf-V)/Shalf))
guess = (-50, 10)
popt, _ = curve_fit(boltzmann, group.x, group.y, guess)
return popt
output = grouped.apply(fit_boltzmann).apply(pd.Series)
In [117]:
print(output.mean())
print(output.std())
In [118]:
Vhalf = output[0].tolist()
rv = st.rv_discrete(values=(Vhalf, [1/len(Vhalf),]*len(Vhalf)))
print("median: {}".format(rv.median()))
print("95% CI: {}".format(rv.interval(0.95)))
In [119]:
slope = output[1].tolist()
rv = st.rv_discrete(values=(slope, [1/len(slope),]*len(slope)))
print("median: {}".format(rv.median()))
print("95% CI: {}".format(rv.interval(0.95)))
In [120]:
# Voltage and slope factor at half-inactivation
grouped = samples[samples['exp']==2].groupby('sample')
def fit_boltzmann(group):
def boltzmann(V, Vhalf, Shalf):
return 1/(1+np.exp((V-Vhalf)/Shalf))
guess = (-50, 10)
popt, _ = curve_fit(boltzmann, group.x, group.y, guess)
return popt
output = grouped.apply(fit_boltzmann).apply(pd.Series)
In [121]:
print(output.mean())
print(output.std())
In [122]:
Vhalf = output[0].tolist()
rv = st.rv_discrete(values=(Vhalf, [1/len(Vhalf),]*len(Vhalf)))
print("median: {}".format(rv.median()))
print("95% CI: {}".format(rv.interval(0.95)))
In [123]:
slope = output[1].tolist()
rv = st.rv_discrete(values=(slope, [1/len(slope),]*len(slope)))
print("median: {}".format(rv.median()))
print("95% CI: {}".format(rv.interval(0.95)))
In [ ]: