Executed: Mon Mar 27 11:38:45 2017

Duration: 7 seconds.

usALEX-5samples - Template

This notebook is executed through 8-spots paper analysis. For a direct execution, uncomment the cell below.



In [1]:

    
ph_sel_name = "None"



In [2]:

    
data_id = "12d"



In [3]:

    
# data_id = "7d"

Load software and filenames definitions



In [4]:

    
from fretbursts import *









    



 - Optimized (cython) burst search loaded.
 - Optimized (cython) photon counting loaded.
--------------------------------------------------------------
 You are running FRETBursts (version 0.5.9).

 If you use this software please cite the following paper:

   FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET
   Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 

--------------------------------------------------------------



In [5]:

    
init_notebook()
from IPython.display import display

Data folder:



In [6]:

    
data_dir = './data/singlespot/'



In [7]:

    
import os
data_dir = os.path.abspath(data_dir) + '/'
assert os.path.exists(data_dir), "Path '%s' does not exist." % data_dir

List of data files:



In [8]:

    
from glob import glob
file_list = sorted(f for f in glob(data_dir + '*.hdf5') if '_BKG' not in f)
## Selection for POLIMI 2012-11-26 datatset
labels = ['17d', '27d', '7d', '12d', '22d']
files_dict = {lab: fname for lab, fname in zip(labels, file_list)}
files_dict









    Out[8]:





{'12d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/007_dsDNA_12d_3nM_green100u_red40u.hdf5',
 '17d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/004_dsDNA_17d_green100u_red40u.hdf5',
 '22d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/008_dsDNA_22d_500pM_green100u_red40u.hdf5',
 '27d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/005_dsDNA_27d_green100u_red40u.hdf5',
 '7d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/006_dsDNA_7d_green100u_red40u.hdf5'}



In [9]:

    
data_id









    Out[9]:





'12d'

Data load

Initial loading of the data:



In [10]:

    
d = loader.photon_hdf5(filename=files_dict[data_id])

Load the leakage coefficient from disk:



In [11]:

    
leakage_coeff_fname = 'results/usALEX - leakage coefficient DexDem.csv'
leakage = np.loadtxt(leakage_coeff_fname)

print('Leakage coefficient:', leakage)









    



Leakage coefficient: 0.10029

Load the direct excitation coefficient ($d_{exAA}$) from disk:



In [12]:

    
dir_ex_coeff_fname = 'results/usALEX - direct excitation coefficient dir_ex_aa.csv'
dir_ex_aa = np.loadtxt(dir_ex_coeff_fname)

print('Direct excitation coefficient (dir_ex_aa):', dir_ex_aa)









    



Direct excitation coefficient (dir_ex_aa): 0.06062

Update d with the correction coefficients:



In [13]:

    
d.leakage = leakage
d.dir_ex = dir_ex_aa

Laser alternation selection

At this point we have only the timestamps and the detector numbers:



In [14]:

    
d.ph_times_t, d.det_t









    Out[14]:





([array([      42190,       56253,       56263, ..., 47999942928,
         47999959323, 48000016759])],
 [array([1, 0, 0, ..., 1, 1, 1], dtype=uint32)])

We need to define some parameters: donor and acceptor ch, excitation period and donor and acceptor excitiations:



In [15]:

    
d.add(det_donor_accept=(0, 1), alex_period=4000, D_ON=(2850, 580), A_ON=(900, 2580), offset=0)

We should check if everithing is OK with an alternation histogram:



In [16]:

    
plot_alternation_hist(d)

If the plot looks good we can apply the parameters with:



In [17]:

    
loader.alex_apply_period(d)









    



# Total photons (after ALEX selection):   1,642,933
#  D  photons in D+A excitation periods:    404,153
#  A  photons in D+A excitation periods:  1,238,780
# D+A photons in  D  excitation period:   1,043,678
# D+A photons in  A  excitation period:     599,255

Measurements infos

All the measurement data is in the d variable. We can print it:



In [18]:

    
d









    Out[18]:





singlespot_007_dsDNA_12d_3nM_green100u_red40u G1.000 Lk10.029 dir6.1

Or check the measurements duration:



In [19]:

    
d.time_max









    Out[19]:





599.99949153749992

Compute background

Compute the background using automatic threshold:



In [20]:

    
d.calc_bg(bg.exp_fit, time_s=60, tail_min_us='auto', F_bg=1.7)









    



 - Calculating BG rates ... [DONE]



In [21]:

    
dplot(d, timetrace_bg)









    Out[21]:





<matplotlib.axes._subplots.AxesSubplot at 0x1235b3be0>



In [22]:

    
d.rate_m, d.rate_dd, d.rate_ad, d.rate_aa









    Out[22]:





([1960.0483965382143],
 [406.04619715470801],
 [902.31032918273252],
 [628.25703861394027])

Burst search and selection



In [23]:

    
d.burst_search(L=10, m=10, F=7, ph_sel=Ph_sel('all'))









    



 - Performing burst search (verbose=False) ...[DONE]
 - Calculating burst periods ...[DONE]
 - Counting D and A ph and calculating FRET ... 
   - Applying background correction.
   - Applying leakage correction.
   - Applying direct excitation correction.
   [DONE Counting D/A]



In [24]:

    
print(d.ph_sel)
dplot(d, hist_fret);

all



In [25]:

    
# if data_id in ['7d', '27d']:
#     ds = d.select_bursts(select_bursts.size, th1=20)
# else:
#     ds = d.select_bursts(select_bursts.size, th1=30)



In [26]:

    
ds = d.select_bursts(select_bursts.size, add_naa=False, th1=30)



In [27]:

    
n_bursts_all = ds.num_bursts[0]



In [28]:

    
def select_and_plot_ES(fret_sel, do_sel):
    ds_fret= ds.select_bursts(select_bursts.ES, **fret_sel)
    ds_do = ds.select_bursts(select_bursts.ES, **do_sel)
    bpl.plot_ES_selection(ax, **fret_sel)
    bpl.plot_ES_selection(ax, **do_sel)    
    return ds_fret, ds_do



In [29]:

    
ax = dplot(ds, hist2d_alex, S_max_norm=2, scatter_alpha=0.1)

if data_id == '7d':
    fret_sel = dict(E1=0.60, E2=1.2, S1=0.2, S2=0.9, rect=False)
    do_sel = dict(E1=-0.2, E2=0.5, S1=0.8, S2=2, rect=True)    
    ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)
    
elif data_id == '12d':
    fret_sel = dict(E1=0.30,E2=1.2,S1=0.131,S2=0.9, rect=False)
    do_sel = dict(E1=-0.4, E2=0.4, S1=0.8, S2=2, rect=False)
    ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)

elif data_id == '17d':
    fret_sel = dict(E1=0.01, E2=0.98, S1=0.14, S2=0.88, rect=False)
    do_sel = dict(E1=-0.4, E2=0.4, S1=0.80, S2=2, rect=False)
    ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)

elif data_id == '22d':
    fret_sel = dict(E1=-0.16, E2=0.6, S1=0.2, S2=0.80, rect=False)
    do_sel = dict(E1=-0.2, E2=0.4, S1=0.85, S2=2, rect=True)
    ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)    

elif data_id == '27d':
    fret_sel = dict(E1=-0.1, E2=0.5, S1=0.2, S2=0.82, rect=False)
    do_sel = dict(E1=-0.2, E2=0.4, S1=0.88, S2=2, rect=True)
    ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)



In [30]:

    
n_bursts_do = ds_do.num_bursts[0]
n_bursts_fret = ds_fret.num_bursts[0]

n_bursts_do, n_bursts_fret









    Out[30]:





(329, 948)



In [31]:

    
d_only_frac = 1.*n_bursts_do/(n_bursts_do + n_bursts_fret)
print('D-only fraction:', d_only_frac)









    



D-only fraction: 0.257635082224



In [32]:

    
dplot(ds_fret, hist2d_alex, scatter_alpha=0.1);



In [33]:

    
dplot(ds_do, hist2d_alex, S_max_norm=2, scatter=False);

Donor Leakage fit



In [34]:

    
bandwidth = 0.03

E_range_do = (-0.1, 0.15)
E_ax = np.r_[-0.2:0.401:0.0002]

E_pr_do_kde = bext.fit_bursts_kde_peak(ds_do, bandwidth=bandwidth, weights='size', 
                                       x_range=E_range_do, x_ax=E_ax, save_fitter=True)



In [35]:

    
mfit.plot_mfit(ds_do.E_fitter, plot_kde=True, bins=np.r_[E_ax.min(): E_ax.max(): bandwidth])
plt.xlim(-0.3, 0.5)
print("%s: E_peak = %.2f%%" % (ds.ph_sel, E_pr_do_kde*100))









    



all: E_peak = 1.60%

Burst sizes



In [36]:

    
nt_th1 = 50



In [37]:

    
dplot(ds_fret, hist_size, which='all', add_naa=False)
xlim(-0, 250)
plt.axvline(nt_th1)









    Out[37]:





<matplotlib.lines.Line2D at 0x12254f8d0>



In [38]:

    
Th_nt = np.arange(35, 120)
nt_th = np.zeros(Th_nt.size)
for i, th in enumerate(Th_nt):
    ds_nt = ds_fret.select_bursts(select_bursts.size, th1=th)
    nt_th[i] = (ds_nt.nd[0]  + ds_nt.na[0]).mean() - th



In [39]:

    
plt.figure()
plot(Th_nt, nt_th)
plt.axvline(nt_th1)









    Out[39]:





<matplotlib.lines.Line2D at 0x1222cf6a0>



In [40]:

    
nt_mean = nt_th[np.where(Th_nt == nt_th1)][0]
nt_mean









    Out[40]:





22.002399108774739

Fret fit

Max position of the Kernel Density Estimation (KDE):



In [41]:

    
E_pr_fret_kde = bext.fit_bursts_kde_peak(ds_fret, bandwidth=bandwidth, weights='size')
E_fitter = ds_fret.E_fitter



In [42]:

    
E_fitter.histogram(bins=np.r_[-0.1:1.1:0.03])



In [43]:

    
E_fitter.fit_histogram(mfit.factory_gaussian(), center=0.5)



In [44]:

    
fig, ax = plt.subplots(1, 2, figsize=(14, 4.5))
mfit.plot_mfit(E_fitter, ax=ax[0])
mfit.plot_mfit(E_fitter, plot_model=False, plot_kde=True, ax=ax[1])
print('%s\nKDE peak %.2f ' % (ds_fret.ph_sel, E_pr_fret_kde*100))
display(E_fitter.params*100)









    



all
KDE peak 74.40 






    






  
    
      
      amplitude
      center
      sigma
    
  
  
    
      0
      98.0359
      73.1546
      9.18958

Weighted mean of $E$ of each burst:



In [45]:

    
ds_fret.fit_E_m(weights='size')









    Out[45]:





array([ 0.72082811])

Gaussian fit (no weights):



In [46]:

    
ds_fret.fit_E_generic(fit_fun=bl.gaussian_fit_hist, bins=np.r_[-0.1:1.1:0.03], weights=None)









    Out[46]:





array([ 0.73141552])

Gaussian fit (using burst size as weights):



In [47]:

    
ds_fret.fit_E_generic(fit_fun=bl.gaussian_fit_hist, bins=np.r_[-0.1:1.1:0.005], weights='size')









    Out[47]:





array([ 0.73132553])



In [48]:

    
E_kde_w = E_fitter.kde_max_pos[0]
E_gauss_w = E_fitter.params.loc[0, 'center']
E_gauss_w_sig = E_fitter.params.loc[0, 'sigma']
E_gauss_w_err = float(E_gauss_w_sig/np.sqrt(ds_fret.num_bursts[0]))
E_kde_w, E_gauss_w, E_gauss_w_sig, E_gauss_w_err









    Out[48]:





(0.74400000000002708,
 0.7315464396396344,
 0.09189583189621753,
 0.002984637868593663)

Stoichiometry fit

Max position of the Kernel Density Estimation (KDE):



In [49]:

    
S_pr_fret_kde = bext.fit_bursts_kde_peak(ds_fret, burst_data='S', bandwidth=0.03) #weights='size', add_naa=True)
S_fitter = ds_fret.S_fitter



In [50]:

    
S_fitter.histogram(bins=np.r_[-0.1:1.1:0.03])
S_fitter.fit_histogram(mfit.factory_gaussian(), center=0.5)



In [51]:

    
fig, ax = plt.subplots(1, 2, figsize=(14, 4.5))
mfit.plot_mfit(S_fitter, ax=ax[0])
mfit.plot_mfit(S_fitter, plot_model=False, plot_kde=True, ax=ax[1])
print('%s\nKDE peak %.2f ' % (ds_fret.ph_sel, S_pr_fret_kde*100))
display(S_fitter.params*100)









    



all
KDE peak 57.48 






    






  
    
      
      amplitude
      center
      sigma
    
  
  
    
      0
      100.772
      55.7524
      10.5052



In [52]:

    
S_kde = S_fitter.kde_max_pos[0]
S_gauss = S_fitter.params.loc[0, 'center']
S_gauss_sig = S_fitter.params.loc[0, 'sigma']
S_gauss_err = float(S_gauss_sig/np.sqrt(ds_fret.num_bursts[0]))
S_kde, S_gauss, S_gauss_sig, S_gauss_err









    Out[52]:





(0.57480000000002218,
 0.5575240857296362,
 0.105051633856861,
 0.0034119184526336392)

The Maximum likelihood fit for a Gaussian population is the mean:



In [53]:

    
S = ds_fret.S[0]
S_ml_fit = (S.mean(), S.std())
S_ml_fit









    Out[53]:





(0.56340887711013254, 0.10161164076993413)

Computing the weighted mean and weighted standard deviation we get:



In [54]:

    
weights = bl.fret_fit.get_weights(ds_fret.nd[0], ds_fret.na[0], weights='size', naa=ds_fret.naa[0], gamma=1.)
S_mean = np.dot(weights, S)/weights.sum()
S_std_dev = np.sqrt(
        np.dot(weights, (S - S_mean)**2)/weights.sum())
S_wmean_fit = [S_mean, S_std_dev]
S_wmean_fit









    Out[54]:





[0.5486196220107683, 0.098086138430599032]

Save data to file



In [55]:

    
sample = data_id

The following string contains the list of variables to be saved. When saving, the order of the variables is preserved.



In [56]:

    
variables = ('sample n_bursts_all n_bursts_do n_bursts_fret '
             'E_kde_w E_gauss_w E_gauss_w_sig E_gauss_w_err S_kde S_gauss S_gauss_sig S_gauss_err '
             'E_pr_do_kde nt_mean\n')

This is just a trick to format the different variables:



In [57]:

    
variables_csv = variables.replace(' ', ',')
fmt_float = '{%s:.6f}'
fmt_int = '{%s:d}'
fmt_str = '{%s}'
fmt_dict = {**{'sample': fmt_str}, 
            **{k: fmt_int for k in variables.split() if k.startswith('n_bursts')}}
var_dict = {name: eval(name) for name in variables.split()}
var_fmt = ', '.join([fmt_dict.get(name, fmt_float) % name for name in variables.split()]) + '\n'
data_str = var_fmt.format(**var_dict)

print(variables_csv)
print(data_str)









    



sample,n_bursts_all,n_bursts_do,n_bursts_fret,E_kde_w,E_gauss_w,E_gauss_w_sig,E_gauss_w_err,S_kde,S_gauss,S_gauss_sig,S_gauss_err,E_pr_do_kde,nt_mean

12d, 1307, 329, 948, 0.744000, 0.731546, 0.091896, 0.002985, 0.574800, 0.557524, 0.105052, 0.003412, 0.016000, 22.002399



In [58]:

    
# NOTE: The file name should be the notebook name but with .csv extension
with open('results/usALEX-5samples-PR-leakage-dir-ex-all-ph.csv', 'a') as f:
    f.seek(0, 2)
    if f.tell() == 0:
        f.write(variables_csv)
    f.write(data_str)