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The below is motivated by a problem I'm observing in my experimental data

I have m boxes, where each box is supposed to contain k molecules of mRNA. The measurement process includes labeling all the molecules with a box-specific tag, mixing them, amplifying them to detectable levels and deconvoluting based on tags. As tag-labeling is a lossy process with estimated efficiency of 5-10% and as we are essentially counting successes a binomial model with n=k and 0.05<=p<=0.1 sounds fitting. Graphing the variance/mean vs. mean shows that it doesn't fit to the straight line expected from a binomial with constant p. If we assume that p is variable, however, things work out well. (see bottom for data)

The intuition is that we are compounding distributions - as an example assume that m/2 boxes are sampled with p=0.05 and m/2 boxes with p=0.1. Then the distribution of all m boxes would be an 'overlap' of 2 binomials each with the same n but different p. Intuitively I would expect the mean to be bound between the mean of the two distributions (as the mean is a point found between the two other points) while I would expect the variance to be larger than either of the two variances (as variance is correlated with the width of the distribution and the 'overlapping' width is by definition longer than each of its components). Simulations support my argument but I don't know how to go about formalizing it and I don't know how to use this to assist is modeling the data. (Esp - how to use this to estimate the variability in tagging efficiency).

What do you say?

The below graph shows real data (each point is generated from 48 boxes with equal k) versus the toy model suggested above (100 boxes with p=0.05 and 100 boxes with p=0.1 for different n).

[EDIT: per comments 'concatenated distribution' was changed to 'compound distribution']

enter image description here

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  • $\begingroup$ My terminology probably is nonstandard, yes (sorry, I'll fix it, 'compound' looks right based on what you say + Wikipedia). Is the terminology why you disagree with the example? Assuming p is chosen from something simple like a uniform distribution between two points or a Gaussian, are you familiar with an analytical expression for the variance? $\endgroup$
    – queryous
    Commented May 16, 2015 at 22:22
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    $\begingroup$ Ah, now I understand what you are doing better. Your example may be fine. Instead of "overlap" I would say "mixture." $\endgroup$
    – Douglas Zare
    Commented May 16, 2015 at 23:00
  • $\begingroup$ I am a little bit surprised this was considered on-topic here. Thr process to decide on/off-topic is somewhat noisy! For the future, such questions are usually better at stats.stackexchange.com $\endgroup$
    – kjetil b halvorsen
    Commented May 17, 2015 at 12:00
  • $\begingroup$ @kjetil b halvorsen: That it might be on-topic on stats.stackexchange.com does not automatically imply that it is off-topic here. I thought about trying to redirect it to stats.stackexchange.com, but decided against it. Since the question had an upvote and was about probability, and the law of total variance is not as well known as it should be, I figured that it would be fine here. $\endgroup$
    – Douglas Zare
    Commented May 17, 2015 at 17:46
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    $\begingroup$ Well, yes, but this isnt really "research-level". It is a standard probability question! $\endgroup$
    – kjetil b halvorsen
    Commented May 17, 2015 at 17:51

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I think you want the law of total variance:

The variance of $\operatorname{Bin}(n,X)$ where $X$ is uniform on $[a,b]$ is $$\begin{eqnarray} \operatorname{Var}(\operatorname{Bin}(n,X))&=&E_X[\operatorname{Var}(\operatorname{Bin}(n,X)|X)] + \operatorname{Var}_X(E[\operatorname{Bin}(n,X)|X])\newline &=& E[nX(1-X)] + \operatorname{Var}(nX) \newline &=& n(E[X]-E[X^2]) + n^2 \operatorname{Var}(X) \newline & =& n \left( \frac{(a+b)}{2} - \frac{(a^2+ab+b^2)}{3}\right) + n^2 \frac{(b-a)^2}{12} \end{eqnarray}$$

For example, if $[a,b] =[1/20,1/10]$, this is $\frac{83n}{1200} + \frac{n^2}{4800} = 0.0691 n + 0.000208n^2$.

If $X$ takes the values $a$ and $b$ with probabilities equal to $1/2$, then

$$\begin{eqnarray} \operatorname{Var}(\operatorname{Bin}(n,X))&=&E_X[\operatorname{Var}(\operatorname{Bin}(n,X)|X)] + \operatorname{Var}_X(E[\operatorname{Bin}(n,X)|X])\newline &=& n\left(\frac{a(1-a)+b(1-b)}{2}\right) + n^2 \frac{(b-a)^2}{4}\end{eqnarray}$$

If $a=1/20$ and $b=1/10$, this is $\frac{11n}{160} + \frac{n^2}{1600} =0.0688 n + 0.000625n^2$.

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  • $\begingroup$ There's a decent chance I'll want to use the above derivation in a supplementary part of a paper, assuming my boss and the journal OK. Is there some kind of convention on citations in stackoverflow? Or is this not the type of thing that needs to be cited? $\endgroup$
    – queryous
    Commented May 17, 2015 at 14:34
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    $\begingroup$ I think this one doesn't need to be cited (you might cite the Wikipedia page or one of its references), but there are some standard styles for citing answers from MathOverflow. If you left-click on the word "share" under the answer, and then click "cite" on the bottom left, you can get an example of a citation of this answer. $\endgroup$
    – Douglas Zare
    Commented May 17, 2015 at 17:35

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