Setting up Python

The data we are going to use as illustrations were recorded by Andreas Pippow Kloppenburg Lab, University of Cologne and are freely available in HDF5 format at the following URLs:

Python 3 is used here so if you want to do the same with Python 2 you should start with:

from __future__ import print_function, division, unicode_literals, absolute_import

Loading the data into Python requires the installation of h5py, a module available in the anaconda distribution.

Loading the POMC data set in Python

We start by downloading the data set on our local disk. To do that we need the urllib module:

In [1]:
from urllib.request import urlretrieve
('Data_POMC.hdf5', <http.client.HTTPMessage at 0x7fbda1e2edd8>)

Python 2 users should type instead:

import urllib

Once the data are on the local disk, they are loaded into Python with:

In [1]:
import h5py
In [2]:
pomc = h5py.File("Data_POMC.hdf5","r")

Information on the content of this HDF5 file can be found in its README attribute:

In [4]:
'POMC data set recorded by Andreas Pippow (Kloppenburg Laboratory Cologne University, 168 measurements performed with a CCD camera recording Fura-2 fluorescence (excitation wavelength: 340 nm). The size of the CCD chip is 60 x 80 pixels. A stimulation (depolarization induced calcium entry) comes at time 527. Details about this data set can be found in: Joucla et al (2013) Estimating background-subtracted fluorescence transients in calcium imaging experiments: A quantitative approach. Cell Calcium. 54 (2): 71-85.'

We create next variables pointing to the time vector and to the image stack:

In [3]:
time_pomc = pomc['time'][...]
stack_pomc = pomc['stack'][...]

We then close the file:

In [4]:

Loading the Calibration data set in Python

We start by downloading the data set on our local disk. To do that we need the urllib module:

In [ ]:

Python 2 users should type instead:


Once the data are on the local disk, they are loaded into Python with:

In [5]:
calibration = h5py.File("CCD_calibration.hdf5","r")

The file is made of 10 groups with the above names. Each group contains 2 datasets:

In [8]:
['stack', 'time']

The dataset stack contains, as its name says, the image stack and its shape is:

In [9]:
(60, 80, 100)

So the CCD captor is made of 60 x 80 pixels and the 100 exposures take the dimension with index 2 (that is the third dimension).

The data set time contains a vector of times at which the different exposure were done:

In [11]:

All this information can be found in the README attribute of the file:

In [12]:
'Imago/SensiCam CCD camera (Till Photonics) calibration data set. Fluorescence measurments were made using a fluorescent plastic slide. 10 exposure times from 10 to 100 ms (each making an HDF5 group) were used. For each exposure time 100 exposures were performed (with 200 ms between each). The fluorescence measured in each of the 60 x 80 pixels of the camera are stored in the stack data set of each group. The time data set (a vector) of each group contains the time at which each illumination was done. These recordings were done by Andreas Pippow (Kloppenburg Laboratory Cologne University, They were used in: Sébastien Joucla, Andreas Pippow, Peter Kloppenburg and Christophe Pouzat (2010) Quantitative estimation of calcium dynamics from ratiometric measurements: A direct, non-ratioing, method. Journal of Neurophysiology 103: 1130-1144.'

loading key modules

We are going to use the usual scientific python modules plus SymPy:

In [6]:
import numpy as np
import matplotlib.pyplot as plt
import sympy as sy
import scipy
%matplotlib inline

Some function definitions

In [7]:
def plotSignal(stack,lw=1):
    import numpy as np
    import matplotlib.pyplot as plt
    n_x, n_y, n_t = stack.shape
    amp_min = np.min(stack)
    amp_max = np.max(stack)
    amp_diff = np.ptp(stack)
    x_domain = np.arange(n_t)/n_t
    y_domain = (0,n_y)
    for r_idx in range(n_x):
        for c_idx in range(n_y):
            y_min = n_x - r_idx - 1
            sig = stack[r_idx,c_idx,:]
            Y = (sig-amp_min)/amp_diff + y_min
            X = x_domain + c_idx

The variability inherent to fluorescence imaging data

In [8]:

ADU counts (raw data) from Fura-2 excited at 340 nm. Each square corresponds to a pixel. 25.05 s of data are shown. Same scale on each sub-plot. Data recorded by Andreas Pippow (Kloppenburg Lab. Cologne University).

In [9]:
plt.xlabel("Time (s)",fontsize=25)
plt.ylabel("ADU count",fontsize=25)
(525, 550)

One of the central pixels of the previous figure.

What do we want? (1)

Given the data set illustrated on the last two slides we might want to estimate parameters like:

  • the peak amplitude
  • the decay time constant(s)
  • the baseline level
  • the whole time course (strictly speaking, a function).

If we have a model linking the calcium dynamics---the time course of the free calcium concentration in the cell---to the fluorescence intensity like: $$\frac{\mathrm{d}Ca_t}{\mathrm{dt}} \left(1 + \kappa_{F}(Ca_t) + \kappa_{E}(Ca_t) \right) + \frac{j(Ca_t)}{v} = 0 \, , $$ where $Ca_t$ stands for $[Ca^{2+}]_{free}$ at time t, $v$ is the volume of the neurite---within which diffusion effects can be neglected---and $$j(Ca_t) \equiv \gamma (Ca_t - Ca_{steady}) \, ,$$ is the model of calcium extrusion---$Ca_{steady}$ is the steady state $[Ca^{2+}]_{free}$--- $$\kappa_{F}(Ca_t) \equiv \frac{F_{total} \, K_{F}}{(K_{F} + Ca_t)^2} \quad \mathrm{and} \quad \kappa_{E}(Ca_t) \equiv \frac{E_{total} \, K_{E}}{(K_{E} + Ca_t)^2} \, ,$$ where $F$ stands for the fluorophore en $E$ for the endogenous buffer.

In the previous slide, assuming that the fluorophore (Fura) parameters: $F_{total}$ and $K_F$ have been calibrated, we might want to estimate:

  • the extrusion parameter: $\gamma$
  • the endogenous buffer parameters: $E_{total}$ and $K_E$

using an equation relating measured fluorescence to calcium: $$Ca_t = K_{F} \, \frac{S_t - S_{min}}{S_{max} - S_t} \, ,$$ where $S_t$ is the fluorescence (signal) measured at time $t$, $S_{min}$ and $S_{max}$ are calibrated parameters corresponding respectively to the fluorescence in the absence of calcium and with saturating $[Ca^{2+}]$ (for the fluorophore).

  • The variability of our signal---meaning that under replication of our measurements under the exact same conditions we wont get the exact same signal---implies that our estimated parameters will also fluctuate upon replication.
  • Formally our parameters are modeled as random variables and it is not enough to summarize a random variable by a single number.
  • If we cannot get the full distribution function for our parameters, we want to give at least ranges within which the true value of the parameter should be found with a given probability.
  • In other words: an analysis without confidence intervals is not an analysis, it is strictly speaking useless since it can't be reproduced---if I say that my time constant is 25.76 ms the probability that upon replication I get again 25.76 is essentially 0; if I say that the actual time constant has a 0.95 probability to be in the interval [24,26.5], I can make a comparison with replications.

A proper handling of the "variability" matters

Let us consider a simple data generation model: $$Y_i \sim \mathcal{P}(f_i)\, , \quad i=0,1,\ldots,K \; ,$$ where $\mathcal{P}(f_i)$ stands for the Poisson distribution with parameter $f_i$ : $$\mathrm{Pr}\{Y_i = n\} = \frac{(f_i)^n}{n!} \exp (-f_i)\, , \quad \mathrm{for} \quad n=0,1,2,\ldots $$ and $$f_i = f(\delta i| f_{\infty}, \Delta, \beta) = f_{\infty} + \Delta \, \exp (- \beta \, \delta i)\; ,$$ δ is a time step and $f_{\infty}$, Δ and β are model parameters.

In [10]:
beta_true = 1.0
f_infinite = 100
Delta = 900
X = np.linspace(0,5*beta_true,51)
Theo = Delta*np.exp(-X*beta_true)+f_infinite
Observations = np.random.poisson(Theo)
plt.xlabel("Time (s)",fontsize=25)
<matplotlib.text.Text at 0x7f79a62d13c8>

Data simulated according to the previous model. We are going to assume that $f_{\infty}$ and $\Delta$ are known and that $(t_1,y_1)$ and $(t_2,y_2)$ are given. We want to estimate $\beta$.

Two estimators

We are going to consider two estimators for $\beta$:

  • The "classical" least square estimator: $$ \tilde{\beta} = \arg \min \tilde{L}(\beta) \; ,$$ where $$ \tilde{L}(\beta) = \sum_j \big( y_j - f(t_j \mid \beta) \big)^2 \; .$$
  • The least square estimator applied to the square root of the data: $$\hat{\beta} = \arg \min \hat{L}(\beta) \; ,$$ where $$ \hat{L}(\beta) = \sum_j \big( \sqrt{y_j} - \sqrt{f(t_j \mid \beta)} \big)^2 \; .$$

We perform an empirical study as follows:

  • We simulate 100,000 experiments such that: $$ (Y_1,Y_2) \sim \big(\mathcal{P}(f(0.3|\beta_0), \mathcal{P}(f(3|\beta_0)\big) \; ,$$ with $\beta_0=1$.
  • For each simulated pair, $(y_1,y_2)^{[k]}$ ($k=1,\ldots,10^5$), we minimize $\tilde{L}(\beta)$ and $\hat{L}(\beta)$ to obtain: $(\tilde{\beta}^{[k]},\hat{\beta}^{[k]})$.
  • We build histograms for $\tilde{\beta}^{[k]}$ and $\hat{\beta}^{[k]}$ as density estimators of our estimators.

Simulations with the two estimators

Definitions of utility functions required for applying Newton's method to the first estimator

We want to minimize: $$\tilde{\beta} = \arg \min \tilde{L}(\beta) \; ,$$ where $$\tilde{L}(\beta) = \big( y_1 - f(t_1 \mid \beta) \big)^2 + \big( y_2 - f(t_2 \mid \beta) \big)^2 \; .$$ In other words, we want the root of the derivative. We can get this derivative by hand or with the SymPy module. We will next use the latter.

In [18]:
x_1,x_2,y_1,y_2,Delta,beta,f_infini = sy.symbols('x_1,x_2,y_1,y_2,Delta,beta,f_infini',real=True)
L_tilde = (y_1 - Delta*sy.exp(-beta*x_1)-f_infini)**2 + (y_2 - Delta*sy.exp(-beta*x_2)-f_infini)**2
$$\left(- \Delta e^{- \beta x_{1}} - f_{infini} + y_{1}\right)^{2} + \left(- \Delta e^{- \beta x_{2}} - f_{infini} + y_{2}\right)^{2}$$
In [19]:
G_tilde = sy.diff(L_tilde,beta)
$$2 \Delta x_{1} \left(- \Delta e^{- \beta x_{1}} - f_{infini} + y_{1}\right) e^{- \beta x_{1}} + 2 \Delta x_{2} \left(- \Delta e^{- \beta x_{2}} - f_{infini} + y_{2}\right) e^{- \beta x_{2}}$$

The following print command is usefull to generate the code:

In [20]:
2*Delta*x_1*(-Delta*exp(-beta*x_1) - f_infini + y_1)*exp(-beta*x_1) + 2*Delta*x_2*(-Delta*exp(-beta*x_2) - f_infini + y_2)*exp(-beta*x_2)
In [21]:
G_prime_tilde = sy.diff(G_tilde,beta)
$$2 \Delta^{2} x_{1}^{2} e^{- 2 \beta x_{1}} + 2 \Delta^{2} x_{2}^{2} e^{- 2 \beta x_{2}} - 2 \Delta x_{1}^{2} \left(- \Delta e^{- \beta x_{1}} - f_{infini} + y_{1}\right) e^{- \beta x_{1}} - 2 \Delta x_{2}^{2} \left(- \Delta e^{- \beta x_{2}} - f_{infini} + y_{2}\right) e^{- \beta x_{2}}$$
In [22]:
2*Delta**2*x_1**2*exp(-2*beta*x_1) + 2*Delta**2*x_2**2*exp(-2*beta*x_2) - 2*Delta*x_1**2*(-Delta*exp(-beta*x_1) - f_infini + y_1)*exp(-beta*x_1) - 2*Delta*x_2**2*(-Delta*exp(-beta*x_2) - f_infini + y_2)*exp(-beta*x_2)

We define next a "constructor" function returning the functions required to implement Newton's method:

In [11]:
def mk_g_dg_tilde(x_1,y_1,x_2,y_2,Delta=900,f_infini=100):
    def g(beta):
        return 2*Delta*x_1*(-Delta*np.exp(-beta*x_1) - \
                            f_infini + y_1)*np.exp(-beta*x_1) + \
                            2*Delta*x_2*(-Delta*np.exp(-beta*x_2) - \
                                         f_infini + y_2)*np.exp(-beta*x_2)
    def dg(beta):
        return 2*Delta**2*x_1**2*np.exp(-2*beta*x_1) + \
            2*Delta**2*x_2**2*np.exp(-2*beta*x_2) - \
            2*Delta*x_1**2*(-Delta*np.exp(-beta*x_1) - \
                            f_infini + y_1)*np.exp(-beta*x_1) - \
                            2*Delta*x_2**2*(-Delta*np.exp(-beta*x_2) - \
                                            f_infini + y_2)*np.exp(-beta*x_2)
    return (g,dg)

We then create the required functions:

In [12]:
g_tilde, dg_tilde = mk_g_dg_tilde(X[3],Observations[3],X[30],Observations[30])

We define next a function implementing Newton's method given an initial guess, a derivative of the target function and a second derivative of the target:

In [13]:
def newton(initial_guess,
    pos = initial_guess
    value = target_d(pos)
    idx = 0
    while idx <= iter_max and abs(value) > tolerance :
        pos -= value/target_dd(pos)
        idx += 1
        value = target_d(pos)
    return (pos,value,idx)

A short test:

In [14]:
(0.97769372257738074, -4.8977199185173959e-07, 3)

Definitions of utility functions required for applying Newton's method to the second estimator

We now want to minimize: $$\hat{\beta} = \arg \min \hat{L}(\beta) \; ,$$ where $$\hat{L}(\beta) = \big( \sqrt{y_1} - \sqrt{f(t_1 \mid \beta)} \big)^2 + \big( \sqrt{y_2} - \sqrt{f(t_2 \mid \beta)} \big)^2 \; .$$ We use Sympy since doing these calculations by hand is rather "heavy":

In [27]:
L_hat = (sy.sqrt(y_1) - sy.sqrt(Delta*sy.exp(-beta*x_1) + f_infini))**2 + (sy.sqrt(y_2) - sy.sqrt(Delta*sy.exp(-beta*x_2) + f_infini))**2
G_hat = sy.diff(L_hat,beta)
$$\frac{\Delta x_{1} e^{- \beta x_{1}}}{\sqrt{\Delta e^{- \beta x_{1}} + f_{infini}}} \left(\sqrt{y_{1}} - \sqrt{\Delta e^{- \beta x_{1}} + f_{infini}}\right) + \frac{\Delta x_{2} e^{- \beta x_{2}}}{\sqrt{\Delta e^{- \beta x_{2}} + f_{infini}}} \left(\sqrt{y_{2}} - \sqrt{\Delta e^{- \beta x_{2}} + f_{infini}}\right)$$
In [30]:
Delta*x_1*(sqrt(y_1) - sqrt(Delta*exp(-beta*x_1) + f_infini))*exp(-beta*x_1)/sqrt(Delta*exp(-beta*x_1) + f_infini) + Delta*x_2*(sqrt(y_2) - sqrt(Delta*exp(-beta*x_2) + f_infini))*exp(-beta*x_2)/sqrt(Delta*exp(-beta*x_2) + f_infini)
In [28]:
G_prime_hat = sy.diff(G_hat,beta)
$$\frac{\Delta^{2} x_{1}^{2} \left(\sqrt{y_{1}} - \sqrt{\Delta e^{- \beta x_{1}} + f_{infini}}\right) e^{- 2 \beta x_{1}}}{2 \left(\Delta e^{- \beta x_{1}} + f_{infini}\right)^{\frac{3}{2}}} + \frac{\Delta^{2} x_{1}^{2} e^{- 2 \beta x_{1}}}{2 \Delta e^{- \beta x_{1}} + 2 f_{infini}} + \frac{\Delta^{2} x_{2}^{2} \left(\sqrt{y_{2}} - \sqrt{\Delta e^{- \beta x_{2}} + f_{infini}}\right) e^{- 2 \beta x_{2}}}{2 \left(\Delta e^{- \beta x_{2}} + f_{infini}\right)^{\frac{3}{2}}} + \frac{\Delta^{2} x_{2}^{2} e^{- 2 \beta x_{2}}}{2 \Delta e^{- \beta x_{2}} + 2 f_{infini}} - \frac{\Delta x_{1}^{2} e^{- \beta x_{1}}}{\sqrt{\Delta e^{- \beta x_{1}} + f_{infini}}} \left(\sqrt{y_{1}} - \sqrt{\Delta e^{- \beta x_{1}} + f_{infini}}\right) - \frac{\Delta x_{2}^{2} e^{- \beta x_{2}}}{\sqrt{\Delta e^{- \beta x_{2}} + f_{infini}}} \left(\sqrt{y_{2}} - \sqrt{\Delta e^{- \beta x_{2}} + f_{infini}}\right)$$
In [29]:
Delta**2*x_1**2*(sqrt(y_1) - sqrt(Delta*exp(-beta*x_1) + f_infini))*exp(-2*beta*x_1)/(2*(Delta*exp(-beta*x_1) + f_infini)**(3/2)) + Delta**2*x_1**2*exp(-2*beta*x_1)/(2*(Delta*exp(-beta*x_1) + f_infini)) + Delta**2*x_2**2*(sqrt(y_2) - sqrt(Delta*exp(-beta*x_2) + f_infini))*exp(-2*beta*x_2)/(2*(Delta*exp(-beta*x_2) + f_infini)**(3/2)) + Delta**2*x_2**2*exp(-2*beta*x_2)/(2*(Delta*exp(-beta*x_2) + f_infini)) - Delta*x_1**2*(sqrt(y_1) - sqrt(Delta*exp(-beta*x_1) + f_infini))*exp(-beta*x_1)/sqrt(Delta*exp(-beta*x_1) + f_infini) - Delta*x_2**2*(sqrt(y_2) - sqrt(Delta*exp(-beta*x_2) + f_infini))*exp(-beta*x_2)/sqrt(Delta*exp(-beta*x_2) + f_infini)

We define next the corresponding constructor function:

In [15]:
def mk_g_dg_hat(x_1,y_1,x_2,y_2,Delta=900,f_infini=100):
    def g(beta):
        return Delta*x_1*(np.sqrt(y_1) - np.sqrt(Delta*np.exp(-beta*x_1) + f_infini))*np.exp(-beta*x_1)/np.sqrt(Delta*np.exp(-beta*x_1) + f_infini) + Delta*x_2*(np.sqrt(y_2) - np.sqrt(Delta*np.exp(-beta*x_2)+ f_infini))*np.exp(-beta*x_2)/np.sqrt(Delta*np.exp(-beta*x_2) +f_infini)
    def dg(beta):
        return Delta**2*x_1**2*(np.sqrt(y_1) - np.sqrt(Delta*np.exp(-beta*x_1) + f_infini))*np.exp(-2*beta*x_1)/(2*(Delta*np.exp(-beta*x_1) + f_infini)**(3/2)) + Delta**2*x_1**2*np.exp(-2*beta*x_1)/(2*(Delta*np.exp(-beta*x_1) + f_infini)) + Delta**2*x_2**2*(np.sqrt(y_2) - np.sqrt(Delta*np.exp(-beta*x_2) + f_infini))*np.exp(-2*beta*x_2)/(2*(Delta*np.exp(-beta*x_2) + f_infini)**(3/2)) + Delta**2*x_2**2*np.exp(-2*beta*x_2)/(2*(Delta*np.exp(-beta*x_2) + f_infini)) - Delta*x_1**2*(np.sqrt(y_1) - np.sqrt(Delta*np.exp(-beta*x_1) + f_infini))*np.exp(-beta*x_1)/np.sqrt(Delta*np.exp(-beta*x_1) + f_infini) - Delta*x_2**2*(np.sqrt(y_2) - np.sqrt(Delta*np.exp(-beta*x_2) + f_infini))*np.exp(-beta*x_2)/np.sqrt(Delta*np.exp(-beta*x_2) + f_infini)
    return (g,dg)

A little test:

In [16]:
g_hat, dg_hat = mk_g_dg_hat(X[3],Observations[3],X[30],Observations[30])    
(1.0226210475375788, -4.1330077138468369e-09, 3)

The simulation

In [17]:
n_rep = int(1e5)
beta_tilde = np.zeros((n_rep,3))
beta_hat = np.zeros((n_rep,3))
for rep_idx in range(n_rep):
    Y = np.random.poisson(Theo[[3,30]])
    g_tilde, dg_tilde = mk_g_dg_tilde(X[3],Y[0],X[30],Y[1])
    beta_tilde[rep_idx,:] = newton(1.0,g_tilde,dg_tilde)
    g_hat, dg_hat = mk_g_dg_hat(X[3],Y[0],X[30],Y[1])    
    beta_hat[rep_idx,:] = newton(1.0,g_hat,dg_hat)

We check that every simulation ended before the maximal allowed number of iteration:

In [18]:
(any(beta_tilde[:,2] == 100),any(beta_hat[:,2] == 100))
(False, False)
In [19]:
def Ffct(beta): 
    return 900 * np.exp(-X[[3,30]]*beta) + 100

def dFfct(beta):
    return -X[[3,30]]*900 * np.exp(-X[[3,30]]*beta)

sd0 = np.sqrt((np.sum(dFfct(1.0)**2*Ffct(1.0))/np.sum(dFfct(1.0)**2)**2))
sd1 = np.sqrt(1.0/np.sum(dFfct(1.0)**2/Ffct(1.0)))
beta_vector = np.linspace(0.6,1.6,501)
useless_stuff = plt.hist([beta_tilde[:,0],beta_hat[:,0]],bins=50,normed=True,histtype='step',lw=2)
plt.title(r'Densities of $\widetilde{\beta}$ and $\widehat{\beta}$',fontsize=25)
[<matplotlib.lines.Line2D at 0x7f79a61b34a8>]

Both histograms are built with 50 bins. $\hat{\beta}$ (green) is clearly better than $\tilde{\beta}$ (blue) since its variance is smaller. The derivation of the theoretical (large sample) densities is given in Joucla et al (2010).

CCD camera noise

CCD basics

Source: L. van Vliet et col. (1998) Digital Fluorescence Imaging Using Cooled CCD Array Cameras (figure 3).

"Noise" sources in CCD

  • The "Photon noise" or "shot noise" arises from the fact the measuring a fluorescence intensity, λ, implies counting photons---unless one changes the laws of Physics there is nothing one can do to eliminate this source of variability (improperly called "noise")---: $$\mathrm{Pr}\{N=n\} = \frac{\lambda^n}{n!} \exp -\lambda\, , \quad n \, = \, 0,1,\ldots\, , \quad \lambda > 0\; .$$
  • The "thermal noise" arises from thermal agitation which "dumps" electrons in potential wells; this "noise" also follows a Poisson distribution but it can be made negligible by cooling down the camera.
  • The "read out noise" arises from the conversion of the number of photo-electrons into an equivalent tension; it follows a normal distribution whose variance is independent of the mean (as long as reading is not done at too high a frequency).
  • The "digitization noise" arises from the mapping of a continuous value, the tension, onto a grid; it is negligible as soon as more than 8 bit are used.

A simple CCD model

  • We can easily obtain a simple CCD model taking into account the two main "noise" sources (photon and read-out).
  • To get this model we are going the fact (a theorem) that when a large number of photon are detected, the Poisson distribution is well approximated by (converges in distribution to) a normal distribution with identical mean and variance: $$\mathrm{Pr}\{N=n\} = \frac{\lambda^n}{n!} \exp -\lambda \approx \mathcal{N}(\lambda,\lambda) \; .$$
  • In other words: $$ N \approx \lambda + \sqrt{\lambda} \, \epsilon \; ,$$ where $\epsilon \sim \mathcal{N}(0,1)$ (follows a standard normal distribution).
  • A read-out noise is added next following a normal distribution with 0 mean and variance $\sigma_{R}^2$.
  • We are therefore adding to the random variable $N$ a new independent random variable $R \sim \mathcal{N}(0,\sigma_{R}^2)$ giving: $$M \equiv N+R \approx \lambda + \sqrt{\lambda+\sigma_{R}^2} \, \epsilon \; ,$$ where the fact that the sum of two independent normal random variables is a normal random variable whose mean is the sum of the mean and whose variance is the sum of the variances has been used.
  • Since the capacity of the photo-electron weels is finite (35000 for the camera used in the first slides) and since the number of photon-electrons will be digitized on 12 bit (4096 levels), a "gain" $G$ smaller than one must be applied if we want to represent faithfully (without saturation) an almost full well.
  • We therefore get: $$Y \equiv G \cdot M \approx G \, \lambda + \sqrt{G^2 \, (\lambda+\sigma_{R}^2)} \, \epsilon \; .$$

For completeness: Convergence in distribution of a Poisson toward a normal rv

We use the moment-generating function and the following theorem (e.g. John Rice, 2007, Mathematical Statistics and Data Analysis, Chap. 5, Theorem A):

  • If the moment-generating function of each element of the rv sequence $X_n$ is $m_n(t)$,
  • if the moment-generating function of the rv $X$ is $m(t)$,
  • if $m_n(t) \rightarrow m(t)$ when $n \rightarrow \infty$ for all $|t| \le b$ where $b > 0$
  • then $X_n \xrightarrow{D} X$.

Lets show that: $$Y_n = \frac{X_n - n}{\sqrt{n}} \; , $$ where $X_n$ follows a Poisson distribution with parameter $n$, converges in distribution towards $Z$ standard normal rv.

We have: $$m_n(t) \equiv \mathrm{E}\left[\exp(Y_n t)\right] \; ,$$ therefore: $$m_n(t) = \sum_{k=0}^{\infty} \exp\left(\frac{k-n}{\sqrt{n}}t\right) \frac{n^k}{k!} \exp(-n) \; ,$$

$$m_n(t) = \exp(-n) \exp(-\sqrt{n}t) \sum_{k=0}^{\infty} \frac{\left(n \exp\left(t/\sqrt{n}\right)\right)^k}{k!}$$ $$m_n(t) = \exp\left(-n - \sqrt{n} t+ n \exp(t/\sqrt{n})\right)$$ $$m_n(t) = \exp\left(-n - \sqrt{n} t+ n \sum_{k=0}^{\infty} \left(\frac{t}{\sqrt{n}}\right)^k \frac{1}{k!}\right)$$ $$m_n(t) = \exp\left(-n - \sqrt{n} t+ n + \sqrt{n} t + \frac{t^2}{2} + n \sum_{k=3}^{\infty} \left(\frac{t}{\sqrt{n}}\right)^k \frac{1}{k!}\right)$$ $$m_n(t) = \exp\left( \frac{t^2}{2} + n \sum_{k=3}^{\infty} \left(\frac{t}{\sqrt{n}}\right)^k \frac{1}{k!}\right)$$

We must show: $$n \sum_{k=3}^{\infty}\left(\frac{t}{\sqrt{n}}\right)^k \frac{1}{k!} \rightarrow_{n \rightarrow \infty} 0 \quad \forall\ |t| \le b, \quad \text{where} \quad b > 0\, ,$$ since $\exp(-t^2/2)$ is the moment-generating function of a standard normal rv. But $$\left| n \sum_{k=3}^{\infty} \left(\frac{t}{\sqrt{n}}\right)^k \frac{1}{k!} \right| \rightarrow_{n \rightarrow \infty} 0 \quad \forall\ |t| \le b, \quad \text{where} \quad b > 0\,$$ implies that since $$- \left|n \sum_{k=3}^{\infty} \left(\frac{t}{\sqrt{n}}\right)^k \frac{1}{k!} \right| \le n \sum_{k=3}^{\infty} \left(\frac{t}{\sqrt{n}}\right)^k \frac{1}{k!} \le \left| n \sum_{k=3}^{\infty} \left(\frac{t}{\sqrt{n}}\right)^k \frac{1}{k!} \right| \, .$$

But for all $|t| \le b$ where $b > 0$

\begin{displaymath} \begin{array}{lcl} 0 \le \left| n \sum_{k=3}^{\infty} \left(\frac{t}{\sqrt{n}}\right)^k \frac{1}{k!} \right| & \le & n \sum_{k=3}^{\infty} \left(\frac{|t|}{\sqrt{n}}\right)^k \frac{1}{k!} \\ & \le & \frac{|t|^3}{\sqrt{n}} \sum_{k=0}^{\infty} \left(\frac{|t|}{\sqrt{n}}\right)^k \frac{1}{(k+3)!} \\ & \le & \frac{|t|^3}{\sqrt{n}} \sum_{k=0}^{\infty} \left(\frac{|t|}{\sqrt{n}}\right)^k \frac{1}{k!} \\ & \le & \frac{|t|^3}{\sqrt{n}} \exp\left(\frac{|t|}{\sqrt{n}}\right) \rightarrow_{n \rightarrow \infty} 0 \, , \end{array} \end{displaymath}

which completes the proof.


We define first a utility function:

In [20]:
from scipy.stats import norm, poisson
ZZ = np.linspace(-3,3,501)
FZ = norm.cdf(ZZ)

def poisson_steps(par,start,end):
    xpairs = [[(i-par)/np.sqrt(par),(i+1-par)/np.sqrt(par)] for i in range(start,end+1)]
    ypairs = [[poisson.cdf(i,par),poisson.cdf(i,par)] for i in range(start,end+1)]
    xlist = []
    ylist = []
    for xends,yends in zip(xpairs,ypairs):
    return [xlist,ylist]
In [21]:
FY = poisson_steps(5,0,25)
[<matplotlib.lines.Line2D at 0x7f79a6477668>]

Cumulative distribution functions (CDF) of $Y_5$ (black) and $Z$ a standard normal (red).

In [22]:
FY = poisson_steps(50,0,500)
[<matplotlib.lines.Line2D at 0x7f79a63fb390>]

Cumulative distribution functions (CDF) of $Y_{50}$ (black) and $Z$ a standard normal (red).

In [23]:
FY = poisson_steps(500,500-67,500+67)
[<matplotlib.lines.Line2D at 0x7f799dfcaf60>]

Cumulative distribution functions (CDF) of $Y_{500}$ (black) and $Z$ a standard normal (red).

In [24]:
FY = poisson_steps(5000,5000-213,5000+213)
[<matplotlib.lines.Line2D at 0x7f799dfc4a20>]

Cumulative distribution functions (CDF) of $Y_{5000}$ (black) and $Z$ a standard normal (red).

CCD calibration

If what I just exposed is correct, with the two (main) "noise" sources, the observations $Y$ (from a CCD pixel) follow: $$Y \sim G \, \lambda + \sqrt{G^2 \, (\lambda+\sigma_{R}^2)} \, \epsilon \; ,$$ where $G$ is the camera gain, $\sigma_{R}^2$ is the read-out variance and $\epsilon$ is a standard normal rv. The values of $G$ and $\sigma_{R}^2$ are specified by the manufacturer for each camera, but experience shows that manufacturers tend to be overoptimistic when it comes to their product performances---they can for instance give an underestimated $\sigma_{R}^2$. Its therefore a good idea to measure these parameters with calibration experiments. Such calibration experiments are also the occasion to check that our simple model is relevant.

  • Our problem becomes: How to test $Y \sim G \, \lambda + \sqrt{G^2 \, (\lambda+\sigma_{R}^2)} \, \epsilon$ ? Or how to set different values for $\lambda$?
  • Let's consider a pixel of our CCD "looking" at a fixed volume of a fluorescein solution with a given (and stable) concentration. We have two ways of modifying λ:
    • Change the intensity $i_{e}$ of the light source exciting the fluorophore.
    • Change the exposure time $\tau$.

We can indeed write our $\lambda$ as: $$\lambda = \phi v c i_{e} \tau \, ,$$ where

  • $v$ is the solution's volume "seen" by a given pixel,
  • $c$ is the fluorophore's concentration,
  • $\phi$ is the quantum yield.

In practice it is easier to vary the exposure time τand that's what was done in the experiments described next... Question: Can you guess what these experiments are?

Sebastien Joucla and myself asked our collaborators from the Kloppenburg lab (Cologne University) to:

  • choose 10 exposure times,
  • for each of the 10 times, perform 100 exposures,
  • for each of the 10 x 100 exposures, record the value $y_{ij}$ of the rv $Y_{ij}$ of CCD's pixel $i,j$.

We introduce a rv $Y_{ij}$ for each pixel because it is very difficult (impossible) to have a uniform intensity ($i_e$) and a uniform volume ($v$) and a uniform quantum yield ($\phi$). We have therefore for each pixel: $$Y_{i,j} \sim G \, p_{i,j} \tau + \sqrt{G^2 \, (p_{i,j} \tau+\sigma_{R}^2)} \, \epsilon_{i,j}\; ,$$ where $p_{i,j} = c \phi_{i,j} v_{i,j} i_{e,i,j}$.

  • If our model is correct we should have for each pixel $i,j$, for a given exposure time, a mean value: $$\bar{y}_{i,j} = \frac{1}{100} \sum_{k=1}^{100} y_{i,j,k} \approx G \, p_{i,j} \tau $$
  • and a variance: $$S_{i,j}^2 = \frac{1}{99} \sum_{k=1}^{100} (y_{i,j,k}-\bar{y}_{i,j})^2 \approx G^2 \, (p_{i,j} \tau+\sigma_{R}^2) \; .$$
  • The graph of $S_{i,j}^2$ vs $\bar{y}_{i,j}$ should be a straight line with slope $G$ ordinate at 0, $G^2 \sigma_{R}^2$.
In [25]:
<matplotlib.colorbar.Colorbar at 0x7f799de98978>

The first exposure of 10 ms (experiment performed by Andreas Pippow, Kloppenburg lag, Cologne University).

CCD calibration: Checking the assumptions

  • The data are going to be analyzed as if the $Y_{i,j,k}$ were IID, but they were sequentially recorded. It is therefore strongly recommended to check that the IID hypothesis is reasonable.
  • The small example of the next figure shows that there are no (obvious) trends.
  • We must also check the correlation function.
In [27]:
plt.xlabel("Time (1 unit = 100 ms)",fontsize=25)

Counts time evolution for three neighboring pixels (10 ms exposure time).

  • If the $Y_{i,j,k}$ are not IID we expect a more or less linear trend---due to bleaching of the dye.
  • Rather then looking at each individual pixel sequence like on the previous slide, we can fit the following linear model model to each pixel: $$Y_{i,j,k} = \beta_0 + \beta_1 k + \sigma \epsilon_{i,j}$$ where the $\epsilon_{i,j} \stackrel{IID}{\sim} \mathcal{N}(0,1)$, and check if $\beta_1$ can be reasonably considered as null; while a trend due to bleaching would give a negative $\beta_1$.
  • Without a trend, the theoretical distribution of $\hat{\beta}_1/\hat{\sigma}_{\beta_1}$---$\hat{\beta}_1$ is the estimate of $\beta_1$ and $\hat{\sigma}_{\beta_1}$ its estimated standard error---is a Student's t distribution with 98 degrees of freedom.
  • Applying this idea to the central pixel of the previous slide we get...
In [28]:
D_matrix = np.transpose(np.array([np.ones(100),np.arange(100)]))
P_matrix = np.linalg.solve(,D_matrix),np.transpose(D_matrix))
Y = calibration['10ms/stack'][31,40,:]
beta =,Y)
In [29]:
Y_hat =,beta)
s2_hat = np.sum((Y-Y_hat)**2)/98
beta_se = np.sqrt(s2_hat*np.linalg.inv(,D_matrix)))
/home/xtof/anaconda3/lib/python3.4/site-packages/IPython/kernel/ RuntimeWarning: invalid value encountered in sqrt
In [30]:
from scipy.stats import t
(-0.017892818267477018, 0.081979226908337921)
In [31]:
plt.xlabel("Time (1 unit = 100 ms)",fontsize=25)
[<matplotlib.lines.Line2D at 0x7f799c3dfef0>]

We get $\hat{\beta}_1 = 0.032$ and a 95 % conf. int. for it is: $[-0.018,0.082]$.

We can use the fact that, under the null hypothesis (no trend): $$\hat{\beta}_1/\hat{\sigma}_{\beta_1} \sim t_{98}$$ by constructing the empirical cumulative distribution function (ECDF) of the 60 x 80 pixels at each exposure time to get the maximal difference (in absolute value) with the theoretical CDF to apply a Kolmogorov test. The critical value of the latter for a 99% level and a sample size of 100 is 0.163. We get the following values:

In [32]:
def linear_fit_stack(stack):
    I,J,K = stack.shape
    D_matrix = np.transpose(np.array([np.ones(K),np.arange(K)]))
    P_matrix = np.linalg.solve(,D_matrix),np.transpose(D_matrix))
    the_inv = np.linalg.inv(,D_matrix))[1,1]
    res = np.zeros((I,J))
    for i in range(I):
        for j in range(J):
            beta =,stack[i,j,:])
            Y_hat =,beta)
            s2_hat = np.sum((stack[i,j,:]-Y_hat)**2)/(K-2)
            res[i,j] = beta[1]/np.sqrt(s2_hat*the_inv)
    return res

b1stats = [linear_fit_stack(calibration[n+'/stack']) for n in list(calibration)]
In [33]:
[[n for n in list(calibration)], [int(1000*np.max(np.abs(np.arange(1,len(b1s)+1)/len(b1s)-t.cdf(b1s,98))))/1000 for b1s in [np.sort(b1.flatten()) for b1 in b1stats]]]
 [0.09, 0.089, 0.116, 0.058, 0.135, 0.209, 0.041, 0.178, 0.153, 0.07]]

The values at 50 and 70 ms are too large.

In [34]:
tt = np.linspace(-4,4,501)
junk = plt.hist(np.concatenate([b1.flatten() for b1 in b1stats]),bins=100,normed=True,color='black')
plt.title(r'Density of all $\hat{\beta}_1/\hat{\sigma}_{\beta_1}$',fontsize=25)
[<matplotlib.lines.Line2D at 0x7f799c30f748>]

Empirical density in black, theoretical one (t with 98 df) in orange.

  • We now look for potential correlations between recording from different pixels.
  • We do that by computing the empirical correlation between pixels $(i,j)$ and $(u,v)$.
  • We get the empirical mean at each pixel (for a given exposure time) that is: $\overline{Y}_{ij} = (1/K) \sum_{k=1}^K Y_{ijk}$.
  • We get the empirical variance: $S^2_{ij} = 1/(K-1) \sum_{k=1}^K (Y_{ijk}-\overline{Y}_{ij})^2$.
  • We then obtain the normalized signal or standard score: $N_{ijk} = (Y_{ijk}-\overline{Y}_{ij})/\sqrt{S^2_{ij}}$.
  • The correlation coefficient is then: $\rho(ij,uv) = 1/(K-1) \sum_{k=1}^K N_{ijk} N_{uvk}$.
  • Under the null hypothesis, no correlation, $\rho(ij,uv) \sim \mathcal{N}(0,1/K)$.
In [35]:
def correlations(stack):
    n_row, n_col, n_time = stack.shape
    n_pixel = n_row*n_col
    result = np.zeros((n_pixel*(n_pixel-1)/2,))
    stack_score = np.copy(stack)
    stack_score = (stack_score - stack_score.mean(2).reshape((n_row,n_col,1)))/stack_score.std(2).reshape((n_row,n_col,1))
    idx = 0
    for i in range(n_pixel-1):
        for j in range(i+1,n_pixel):
            pos1 = (i//n_col,i-(i//n_col)*n_col)
            pos2 = (j//n_col,j-(j//n_col)*n_col)
            coef = np.sum(stack_score[pos1[0],pos1[1],:]*stack_score[pos2[0],pos2[1],:])/n_time
            result[idx] = coef
            idx += 1 
    return result
In [36]:
corr10 = correlations(calibration['10ms/stack'])
In [37]:
from scipy.stats import norm
hist_corr10 = plt.hist(corr10,bins=100,normed=True,color='black')
plt.title('Density of correlation coefficients at 10 ms',fontsize=25)
[<matplotlib.lines.Line2D at 0x7f799c3eaeb8>]

Empirical density in black, theoretical one, $\mathcal{N}(0,0.01)$, in orange.

The empirical variance (x 100 and rounded to the third decimal) of the samples of correlation coefficients (1 sample per exposure duration) are:

In [38]:
var_of_corr_list = [np.var(correlations(calibration[n+'/stack'])) for n in list(calibration)]
[[n for n in list(calibration)], [int(100000*v)/1000 for v in var_of_corr_list]]
 [1.009, 1.01, 1.009, 1.01, 1.01, 1.01, 1.009, 1.01, 1.009, 1.009]]

We wrote previously :

  • If our model is correct we should have for each pixel $i,j$, for a given exposure time, a mean value: $$\bar{y}_{i,j} = \frac{1}{100} \sum_{k=1}^{100} y_{i,j,k} \approx G \, p_{i,j} \tau $$
  • and a variance: $$S_{i,j}^2 = \frac{1}{99} \sum_{k=1}^{100} (y_{i,j,k}-\bar{y}_{i,j})^2 \approx G^2 \, (p_{i,j} \tau+\sigma_{R}^2) \; .$$
  • The graph of $S_{i,j}^2$ vs $\bar{y}_{i,j}$ should be a straight line with slope $G$ ordinate at 0, $G^2 \sigma_{R}^2$.
In [39]:
for x,y in zip([np.mean(calibration[n+'/stack'],2) for n in list(calibration)],
               [np.var(calibration[n+'/stack'],2) for n in list(calibration)]):

(0, 1000)

We do see the expected linear relation: $\mathrm{Var}[ADU] = G^2 \sigma_{R}^2 + G \mathrm{E}[ADU]$.

Linear fit

The heteroscedasticity (inhomogeneous variance) visible on the graph is also expected since the variance of a variance for an IID sample of size $K$ from a normal distribution with mean $\mu$ and variance $\sigma^2$ is: $$\mathrm{Var}[S^2] = \frac{2\sigma^4}{(K-1)} \; .$$

  • This means than when we do our linear fit, $$y_k = a + b x_k + \sigma_k \epsilon_k \, ,$$ we should use weights.
  • Here $$x_k = \overline{ADU}_k \quad y_k = \mathrm{Var}[ADU]_k \, ,$$ $$b = G \quad a = G^2 \sigma_R^2 \, .$$
  • It's easy to show that the least square estimates are: $$\hat{a} = \frac{1}{Z} \sum_k \frac{y_k-\hat{b} x_k}{\sigma_k^2} \quad \text{where} \quad Z = \sum_k \frac{1}{\sigma_k^2}$$ and $$\hat{b} = \left(\sum_k \frac{x_k}{\sigma_k^2} \left(y_k - \frac{1}{Z}\sum_j \frac{y_j}{\sigma_j^2}\right)\right) / \left(\sum_k \frac{x_k}{\sigma_k^2}\left(x_k - \frac{1}{Z}\sum_j \frac{x_j}{\sigma_j^2}\right)\right) \, .$$
  • We don't know $\sigma_k$ but we have an estimation: $\hat{\sigma}_k^2 = \mathrm{Var}[S_k^2]$ we can "plug-in" this value to get our weights.
In [40]:
X_k = np.concatenate([x.flatten() for x in [np.mean(calibration[n+'/stack'],2) for n in list(calibration)]])
y_k = np.concatenate([x.flatten() for x in [np.var(calibration[n+'/stack'],2) for n in list(calibration)]])
sigma2_k = 2*y_k**2/(calibration['10ms/stack'].shape[2]-1)
Z = sum(1/sigma2_k)
num1 = sum(X_k/sigma2_k*(y_k-sum(y_k/sigma2_k)/Z))
denom1 = sum(X_k/sigma2_k*(X_k-sum(X_k/sigma2_k)/Z))
b_hat0 = num1/denom1
a_hat0 = sum((y_k-b_hat0*X_k)/sigma2_k)/Z
In [41]:
for x,y in zip([np.mean(calibration[n+'/stack'],2) for n in list(calibration)],
               [np.var(calibration[n+'/stack'],2) for n in list(calibration)]):

aa = np.linspace(0,4500)
(0, 1000)
In [42]:
G_hat0 = b_hat0
S2_hat0 = a_hat0/b_hat0**2 
(G_hat0, S2_hat0)
(0.1384399188990568, 289.85755480280824)

We have here $\hat{G} = 0.14$ and $\hat{\sigma}_R^2 = 290$.

Checking the fit

In [43]:
for x,y in zip([np.mean(calibration[n+'/stack'],2) for n in list(calibration)],
               [np.var(calibration[n+'/stack'],2) for n in list(calibration)]):

plt.xlabel('Fitted value',fontsize=25)
plt.ylabel("Normalized residuals (Normal scores)",fontsize=25)
<matplotlib.lines.Line2D at 0x7f79a6401908>

Some remarks

  • When we use a linear regression, we are (implicitly) assuming that the "independent" variable, here $\overline{ADU}_k$, is exactly known.
  • This was clearly not the case here since $\overline{ADU}_k$ was measured (with an error).
  • We could and will therefore refine our fit.

Error propagation and variance stabilization

Error propagation

  • Let us consider two random variables: $Y$ and $Z$ such that:
  • $Y \approx \mathcal{N}(\mu_Y,\sigma^2_Y)$ or $Y \approx \mu_Y + \sigma_Y \, \epsilon$
  • $Z = f(Y)$, with $f$ continuous and differentiable.
  • Using a first order Taylor expansion we then have:$$ \begin{array}{lcl} Z & \approx & f(\mu_Y + \sigma_Y \, \epsilon) \\ & \approx & f(\mu_Y) + \sigma_Y \, \epsilon \, \frac{d f}{d Y}(\mu_Y) \end{array}$$
  • $\mathrm{E}Z \approx f(\mu_Y) = f(\mathrm{E}Y)$
  • $\mathrm{Var}Z \equiv \mathrm{E}[(Z-\mathrm{E}Z)^2] \approx \sigma^2_Y \, \frac{d f}{d Y}^2(\mu_Y)$
  • $Z \approx f(\mu_Y) + \sigma_Y\left| \frac{d f}{d Y}(\mu_Y)\right| \, \epsilon$

Variance stabilization

  • For our CCD model we have (for a given pixel): $$Y \sim G \, \lambda + \sqrt{G^2 \, (\lambda+\sigma_{R}^2)} \, \epsilon = \mu_Y + \sqrt{G \, \mu_Y + G^2 \, \sigma_{R}^2} \, \epsilon \, .$$
  • Then if $Z = f(Y)$ we get: $$Z \approx f(\mu_Y) + \mid f'(\mu_Y) \mid G \sqrt{\mu_Y / G+\sigma_{R}^2} \, \epsilon$$
  • What happens then if we take: \$f(x) = 2   \sqrt{x/G + \sigma_{R}^2} \$?
  • We have: $$f'(x) = \frac{1}{G \sqrt{ x / G + \sigma_{R}^2}}$$
  • Leading to: $$Z \approx 2 \, \sqrt{\mu_Y / G + \sigma_{R}^2} + \epsilon$$
In [44]:
for x,y in zip([np.mean(2*np.sqrt(calibration[n+'/stack']/G_hat0+S2_hat0),2) for n in list(calibration)],
               [np.var(2*np.sqrt(calibration[n+'/stack']/G_hat0+S2_hat0),2) for n in list(calibration)]):

plt.xlabel(r'$\mathrm{E}(2 \sqrt{ADU/\hat{G}+\hat{\sigma}_R^2})$',fontsize=20)
plt.ylabel(r'$\mathrm{Var}(2 \sqrt{ADU/\hat{G}+\hat{\sigma}_R^2})$',fontsize=20)
<matplotlib.lines.Line2D at 0x7f79a648d9e8>