def estimate_phase_dev(cell, temperature):
"""
Final estimate for the circadian phase deviation.
Parameters
----------
cell : string
Cell condition.
temperature : int
Temperature condition.
Returns
-------
The standard deviation for the phase progression, and the periods.
"""
######### CORRECTION BECAUSE NOT ENOUGH TRACES AT 34°C AND 40°C #########
print('CAUTION : Parameters for None temperature selected since not enough \
traces at 34°C and 40°C')
temperature = None
##################### LOAD DATA ##################
if cell == 'NIH3T3':
path = "Data/NIH3T3.ALL.2017-04-04/ALL_TRACES_INFORMATION.p"
dataClass=LoadData(path, 10000000, temperature = temperature,
division = False)
(ll_area, ll_signal, ll_nan_circadian_factor, ll_obs_phi, ll_peak,
ll_idx_cell_cycle_start, T_theta, T_phi) = \
dataClass.load(load_annotation=True)
#print(len(ll_area))
std, std_T = estimate_phase_dev_from_signal(ll_peak)
elif cell == 'U2OS':
path = "Data/U2OS-2017-03-20/ALL_TRACES_INFORMATION_march_2017.p"
dataClass=LoadData(path, 10000000, temperature = temperature,
division = True)
(ll_area, ll_signal, ll_nan_circadian_factor, ll_obs_phi, ll_peak,
ll_idx_cell_cycle_start, T_theta, T_phi) = \
dataClass.load(load_annotation=True)
std, std_T = estimate_phase_dev_from_signal(ll_peak)
#correction for the neglected coupling since dividing traces
std = std*0.65
std_T = std_T *0.65
else:
print("Cell type doesn't exist")
'''
for (idx, l_signal), l_peak in zip(enumerate(ll_signal), ll_peak):
plt.plot(l_signal)
plt.plot(l_peak)
plt.show()
plt.close()
if idx>17:
break
'''
return std, std_T
def estimate_OU_par(cell, temperature, W=None, gamma_A=0.03, gamma_B=0.03):
"""
Estimate mean and variance of OU processes given a set of conditions,
according to which a set of traces is filtered.
Parameters
----------
cell : string
Cell type.
temperature : integer
Temperature condition.
W : list
Waveform.
gamma_A : float
Regression parameter for the amplitude.
gamma_b : float
Regression parameter for the background.
Returns
-------
The mean and standard deviations of the amplitude and the background.
"""
######### CORRECTION BECAUSE NOT ENOUGH TRACES AT 34°C AND 40°C #########
print(
'CAUTION : Parameters for None temperature selected since not enough \
traces at 34°C and 40°C')
temperature = None
##################### LOAD DATA ################
if cell == 'NIH3T3':
path = "Data/NIH3T3.ALL.2017-04-04/ALL_TRACES_INFORMATION.p"
dataClass = LoadData(path,
10000000,
temperature=temperature,
division=False)
elif cell == 'U2OS':
path = "Data/U2OS-2017-03-20/ALL_TRACES_INFORMATION_march_2017.p"
dataClass = LoadData(path,
10000000,
temperature=temperature,
division=True)
try:
(ll_area, ll_signal, ll_nan_circadian_factor, ll_obs_phi, ll_peak,
ll_idx_cell_cycle_start, T_theta, T_phi) = \
dataClass.load(load_annotation=True)
except:
dataClass.path = '../' + dataClass.path
(ll_area, ll_signal, ll_nan_circadian_factor, ll_obs_phi, ll_peak,
ll_idx_cell_cycle_start, T_theta, T_phi) = \
dataClass.load(load_annotation=True)
return estimate_OU_par_from_signal(ll_signal, W, gamma_A, gamma_B)
def show_no_temperature_difference():
"""
Plot the estimate of the phase at different temperatures.
"""
l_var = []
l_var_var = []
l_temp = [34,37,40]
for temperature in l_temp:
path = "Data/NIH3T3.ALL.2017-04-04/ALL_TRACES_INFORMATION.p"
dataClass=LoadData(path, 10000000, temperature = temperature,
division = False)
(ll_area, ll_signal, ll_nan_circadian_factor, ll_obs_phi, ll_peak, \
ll_idx_cell_cycle_start, T_theta, T_phi) = \
dataClass.load(load_annotation=True)
var,var_var = compute_phase_variance_with_confidence(ll_peak)
l_var.append(var)
l_var_var.append(var_var)
plt.errorbar(l_temp, l_var, yerr = l_var_var, fmt='o')
plt.xlim([33,41])
plt.xlabel("Temperature")
plt.ylabel("Phase diffusion variance mean and deviation")
plt.savefig('Results/RawData/var_diffusion.pdf')
plt.show()
plt.close()
def estimate_cycle_dev(cell, temperature):
"""
Final estimate for the cell-cycle phase deviation.
Parameters
----------
cell : string
Cell condition.
temperature : int
Temperature condition.
Returns
-------
The standard deviation for the phase progression, and the periods.
"""
##################### LOAD DATA ##################
if cell == 'NIH3T3':
path = "Data/NIH3T3.ALL.2017-04-04/ALL_TRACES_INFORMATION.p"
elif cell == 'U2OS':
path = "Data/U2OS-2017-03-20/ALL_TRACES_INFORMATION_march_2017.p"
dataClass=LoadData(path, 10000000, temperature = temperature,
division = True)
(ll_area, ll_signal, ll_nan_circadian_factor, ll_obs_phi, ll_peak, \
ll_idx_cell_cycle_start, T_theta, T_phi) = \
dataClass.load(load_annotation=True)
std, std_T = estimate_phase_dev_from_div(ll_idx_cell_cycle_start)
return std, std_T
def compute_likelihood_sigma():
"""
Compute and plot the likelihood of the phase diffusion parameter,
depending on the temperature.
"""
l_T = [34,37,40]
l_likelihood_T = []
mean_IG = 24
domain_sigma = np.linspace(0.05, 0.3, 100)
for T in l_T:
path = "Data/NIH3T3.ALL.2017-04-04/ALL_TRACES_INFORMATION.p"
dataClass=LoadData(path, 10000000, temperature = T, division = False)
(ll_area, ll_signal, ll_nan_circadian_factor, ll_obs_phi, ll_peak,
ll_idx_cell_cycle_start, T_theta, T_phi) = \
dataClass.load(load_annotation=True)
l_T_clock=[]
for l_peak in ll_peak:
l_idx_peak = [idx for idx, i in enumerate(l_peak) if i==1]
for t_peak_1, t_peak_2 in zip(l_idx_peak[:-1], l_idx_peak[1:]):
#remove outliers
T = (t_peak_2-t_peak_1)/2
if T<12 or T>38:
#double or missing annotation
pass
else:
l_T_clock.append(T )
l_likelihood = []
for sigma_theta in domain_sigma:
lpx = np.log(1/sigma_theta)
#lpx = 1
for T in l_T_clock:
lpx = lpx + np.log(invgauss(T, mean_IG,
4*np.pi**2/sigma_theta**2))
l_likelihood.append( lpx/len(l_T_clock))
l_likelihood_T.append(l_likelihood)
plt.plot(domain_sigma,l_likelihood_T[0], c = 'red', label = '34' )
plt.plot(domain_sigma,l_likelihood_T[1], c = 'blue',label = '37' )
plt.plot(domain_sigma,l_likelihood_T[2], c = 'orange',label = '40' )
plt.axvline(domain_sigma[np.argmax(l_likelihood_T[0])], c= 'red')
plt.axvline(domain_sigma[np.argmax(l_likelihood_T[1])], c= 'blue')
plt.axvline(domain_sigma[np.argmax(l_likelihood_T[2])], c= 'orange')
plt.ylabel(r'$log(L(\sigma_\theta))$')
plt.xlabel(r'$\sigma_\theta$' )
plt.legend()
plt.savefig('Results/RawData/likelihood_sigma_theta.pdf')
plt.show()
plt.close()
def plot_hist_periods(cell, temperature, division):
"""
Given a cell condition, compute and plot a histgram of periods.
Parameters
----------
cell : string
Cell condition.
"""
##################### LOAD DATA ##################
if cell == 'NIH3T3':
path = "../Data/NIH3T3.ALL.2017-04-04/ALL_TRACES_INFORMATION.p"
else:
path = "../Data/U2OS-2017-03-20/ALL_TRACES_INFORMATION_march_2017.p"
dataClass = LoadData(path,
10000000,
temperature=temperature,
division=division)
(ll_area, ll_signal, ll_nan_circadian_factor, ll_obs_phi, ll_peak,
ll_idx_cell_cycle_start, T_theta, T_phi) \
= dataClass.load(load_annotation=True)
if division:
##################### COMPUTE CELL CYCLE DISTRIBUTION ##################
l_T_cell_cycle = []
for l_div_index in ll_idx_cell_cycle_start:
for t1, t2 in zip(l_div_index[:-1], l_div_index[1:]):
l_T_cell_cycle.append((t2 - t1) / 2)
##################### COMPUTE CIRCADIAN CLOCK DISTRIBUTION #################
l_T_clock = []
for l_peak in ll_peak:
l_idx_peak = [idx for idx, i in enumerate(l_peak) if i == 1]
for t_peak_1, t_peak_2 in zip(l_idx_peak[:-1], l_idx_peak[1:]):
l_T_clock.append((t_peak_2 - t_peak_1) / 2)
##################### PLOT BOTH DISTRIBUTIONS ##################
bins = np.linspace(8, 38, 40)
if division:
plt.hist(l_T_cell_cycle, bins, alpha=0.5, label='cell-cycle')
plt.hist(l_T_clock, bins, alpha=0.5, label='clock')
plt.legend(loc='upper right')
if division:
plt.savefig('../Results/RawData/Distributions_div_'+str(temperature)\
+"_"+cell+'.pdf', bbox_inches='tight')
else:
plt.savefig('../Results/RawData/Distributions_nodiv_'+str(temperature)\
+"_"+cell+'.pdf', bbox_inches='tight')
plt.close()
def plot_raw_traces(cell, temperature, division, nb_traces = 10):
##################### LOAD DATA ##################
if cell == 'NIH3T3':
path = "../Data/NIH3T3.ALL.2017-04-04/ALL_TRACES_INFORMATION.p"
else:
path = "../Data/U2OS-2017-03-20/ALL_TRACES_INFORMATION_march_2017.p"
dataClass=LoadData(path, nb_traces, temperature = temperature,
division = division, several_cell_cycles = False)
(ll_area, ll_signal, ll_nan_circadian_factor,
ll_obs_phi, ll_peak, ll_idx_cell_cycle_start,
T_theta, T_phi) = dataClass.load(load_annotation=True)
if division:
#keep only long traces and crop them
to_keep = [i for i, l_signal in enumerate(ll_signal)\
if len(l_signal)>100]
ll_signal = [ll_signal[i][:100] for i in to_keep]
ll_area = [ll_area[i][:100] for i in to_keep]
ll_nan_circadian_factor = \
[ll_nan_circadian_factor[i][:100] for i in to_keep]
ll_obs_phi = [ll_obs_phi[i][:100] for i in to_keep]
ll_peak = [ll_peak[i][:100] for i in to_keep]
ll_idx_cell_cycle_start = [ [j for j in ll_idx_cell_cycle_start[i] \
if j<100] for i in to_keep]
zp = zip(enumerate(ll_signal), ll_nan_circadian_factor, ll_peak,
ll_idx_cell_cycle_start)
for (idx, l_signal), l_nan_circadian_factor, l_peak, l_idx_cc in zp:
tspan = np.linspace(0,len(l_signal)/2, len(l_signal))
l_idx_peak = [idx for idx, i in enumerate(l_peak) if i>0]
l_t_nan_circadian = []
i_temp = None
for idxx, (i1, i2) in enumerate(zip(l_nan_circadian_factor[:-1],
l_nan_circadian_factor[1:] )):
if not i1 and i2:
i_temp = (idxx+1)/2
if i1 and i2:
pass
if i1 and not i2:
if i_temp == None:
print('BUG')
else:
l_t_nan_circadian.append((i_temp, idxx/2))
i_temp = None
plt.subplot(210+ idx%2+1 )
plt.plot(tspan, l_signal, '.', color = 'red')
for p in l_idx_peak:
#plt.axvline(p/2)
pass
for d in l_idx_cc:
plt.axvline(d/2, color = 'black')
for a,b in l_t_nan_circadian:
#plt.axvspan(a, b, color='lightblue', alpha=0.5, lw=0)
pass
if idx%2==1:
plt.tight_layout()
plt.savefig('../Results/RawData/RawTrace_div_'+str(idx)+'_'\
+str(temperature)+"_"+cell+'.pdf')
plt.show()
plt.close()
else:
#keep only long traces and crop them so they all have the same size
#(CAUTION, CODE FOR PEAK NOT CORRECT)
ll_signal = [l_signal[:100] for l_signal in ll_signal \
if len(l_signal)>100]
#ll_peak= [l_peak[:100] for l_signal, l_peak in zip(ll_signal, ll_peak)\
#if len(l_signal)>100]
for (idx, l_signal), l_peak, in zip(enumerate(ll_signal), ll_peak):
tspan = np.linspace(0,len(l_signal)/2, len(l_signal))
l_idx_peak = [idx for idx, i in enumerate(l_peak) if i>0]
plt.subplot(210+ idx%2+1 )
plt.plot(tspan, l_signal, '.', color = 'orange')
for p in l_idx_peak:
#plt.axvline(p/2)
pass
if idx%2==1:
plt.tight_layout()
plt.savefig('../Results/RawData/RawTrace_nodiv_'+str(idx)+'_'\
+str(temperature)+"_"+cell+'.pdf')
plt.show()
plt.close()
