# Likelihood Calculations for vsn

## Introduction

This vignette contains the computations that underlie the numerical code of vsn. If you are a new user and looking for an introduction on how to use vsn, please refer to the vignette Robust calibration and variance stabilization with vsn, which is provided separately.

## Setup and Notation

Consider the model:

\[\begin{equation} \text{arsinh}\left(f(b_i)\cdot y_{ki}+a_i\right) = \mu_k + \varepsilon_{ki} (\#eq:model) \end{equation}\]

where \(\mu_k\), for \(k=1,\ldots,n\), and \(a_i\), \(b_i\), for \(i=1,\ldots,d\) are real-valued parameters, \(f\) is a function \(\mathbb{R}\to\mathbb{R}\) (see below), and \(\varepsilon_{ki}\) are i.i.d. Normal with mean 0 and variance \(\sigma^2\). \(y_{ki}\) are the data. In applications to \(\mu\)array data, \(k\) indexes the features and \(i\) the arrays and/or colour channels.

Examples for \(f\) are \(f(b)=b\) and \(f(b)=e^b\). The former is the most obvious choice; in that case we will usually need to require \(b_i>0\). The choice \(f(b)=e^b\) assures that the factor in front of \(y_{ki}\) is positive for all \(b\in\mathbb{R}\), and as it turns out, simplifies some of the computations.

In the following calculations, I will also use the notation

\[\begin{align} Y \equiv Y(y,a,b) &= f(b)\cdot y+a\\ h \equiv h(y,a,b) &= \text{arsinh}\left(f(b)\cdot y+a\right). \end{align}\]

The probability of the data \((y_{ki})_{k=1\ldots n,\;i=1\ldots d}\) lying in a certain volume element of \(y\)-space (hyperrectangle with sides \([y_{ki}^\alpha,y_{ki}^\beta]\)) is

\[\begin{equation} P=\prod_{k=1}^n\prod_{i=1}^d \int\limits_{y_{ki}^\alpha}^{y_{ki}^\beta} dy_{ki}\;\; p_{\text{Normal}}(h(y_{ki}),\mu_k,\sigma^2)\;\; \frac{dh}{dy}(y_{ki}), \end{equation}\] where \(\mu_k\) is the expectation value for feature \(k\) and \(\sigma^2\) the variance.

With

\[\begin{equation} p_{\text{Normal}}(x,\mu,\sigma^2)=\frac{1}{\sqrt{2\pi\sigma^2}} \exp\left(-\frac{(x-\mu)^2}{2\sigma^2}\right) \end{equation}\]

the likelihood is

\[\begin{equation} L=\left(\frac{1}{\sqrt{2\pi\sigma^2}}\right)^{nd} \prod_{k=1}^n \prod_{i=1}^d \exp\left(-\frac{(h(y_{ki})-\mu_k)^2}{2\sigma^2}\right) \cdot\frac{dh}{dy}(y_{ki})\,. (\#eq:likelihood) \end{equation}\]

For the following, I will need the derivatives

\[\begin{align} \frac{\partial Y}{\partial a}&=1\\ \frac{\partial Y}{\partial b}&=y\cdot f'(b)\\ \frac{dh}{dy}&= \frac{f(b)}{\sqrt{1+(f(b)y+a)^2}}= \frac{f(b)}{\sqrt{1+Y^2}},\\ \frac{\partial h}{\partial a}&=\frac{1}{\sqrt{1+Y^2}},\\ \frac{\partial h}{\partial b}&=\frac{y}{\sqrt{1+Y^2}}\cdot f'(b). \end{align}\]

Note that for \(f(b)=b\), we have \(f'(b)=1\), and for \(f(b)=e^b\), \(f'(b)=f(b)=e^b\).

## Likelihood for Incremental Normalization

Here, incremental normalization means that the model parameters \(\mu_1,\ldots,\mu_n\) and \(\sigma^2\) are already known from a fit to a previous set of \(\mu\)arrays, i.,e. a set of reference arrays. See Section @ref(sec:prof) for the profile likelihood approach that is used if \(\mu_1,\ldots,\mu_n\) and \(\sigma^2\) are not known and need to be estimated from the same data. Versions \(\ge2.0\) of the vsn package implement both of these approaches; in versions \(1.X\) only the profile likelihood approach was implemented, and it was described in the initial publication (Huber et al. 2002).

First, let us note that the likelihood @ref(eq:likelihood) is simply a product of independent terms for different \(i\). We can optimize the parameters \((a_i,b_i)\) separately for each \(i=1,\ldots,d\). From the likelihood @ref(eq:likelihood) we get the \(i\)-th negative log-likelihood

\[\begin{align} -\log(L) &=\sum_{i=1}^d -LL_i\\ -LL_i&=\frac{n}{2}\log\left(2\pi\sigma^2\right)+ \sum_{k=1}^n \left(\frac{(h(y_{ki})-\mu_k)^2}{2\sigma^2} +\log\frac{\sqrt{1+Y_{ki}^2}}{f(b_i)}\right)\\ &=\frac{n}{2}\log\left(2\pi\sigma^2\right) -n\log f(b_i) +\sum_{k=1}^n\left(\frac{(h(y_{ki})-\mu_k)^2}{2\sigma^2} +\frac{1}{2}\log\left(1+Y_{ki}^2\right)\right) (\#eq:nll) \end{align}\]

This is what we want to optimize as a function of \(a_i\) and \(b_i\). The optimizer benefits from the derivatives. The derivative with respect to \(a_i\) is

\[\begin{align} \frac{\partial}{\partial a_i}(-LL_i) &= \sum_{k=1}^n \left( \frac{h(y_{ki})-\mu_k}{\sigma^2} +\frac{Y_{ki}}{\sqrt{1+Y_{ki}^2}} \right) \cdot\frac{1}{\sqrt{1+Y_{ki}^2}} \nonumber\\ &= \sum_{k=1}^n \left(\frac{r_{ki}}{\sigma^2}+A_{ki}Y_{ki}\right)A_{ki} (\#eq:ddanll) \end{align}\]

and with respect to \(b_i\)

\[\begin{align} \frac{\partial}{\partial b_i}(-LL_i) &= -n\frac{f'(b_i)}{f(b_i)} +\sum_{k=1}^n \left( \frac{h(y_{ki})-\mu_k}{\sigma^2} +\frac{Y_{ki}}{\sqrt{1+Y_{ki}^2}}\right) \cdot\frac{y_{ki}}{\sqrt{1+Y_{ki}^2}}\cdot f'(b_i) \nonumber\\ &= -n\frac{f'(b_i)}{f(b_i)} +f'(b_i)\sum_{k=1}^n \left(\frac{r_{ki}}{\sigma^2}+A_{ki}Y_{ki}\right) A_{ki}y_{ki} (\#eq:ddbnll) \end{align}\]

Here, I have introduced the following shorthand notation for the “intermediate results” terms

\[\begin{align} r_{ki}&= h(y_{ki})-\mu_k\\ A_{ki}&=\frac{1}{\sqrt{1+Y_{ki}^2}}. \end{align}\]

Variables for these intermediate values are also used in the C code to organise the computations of the gradient.

## Profile Likelihood

If \(\mu_1,\ldots,\mu_n\) and \(\sigma^2\) are not already known, we can plug in their maximum likelihood estimates, obtained from optimizing \(LL\) for \(\mu_1,\ldots,\mu_n\) and \(\sigma^2\):

\[\begin{align} \hat{\mu}_k &= \frac{1}{d}\sum_{j=1}^d h(y_{kj}) (\#eq:muhat)\\ \hat{\sigma}^2 &= \frac{1}{nd}\sum_{k=1}^n\sum_{j=1}^d (h(y_{kj})-\hat{\mu}_k)^2 (\#eq:sigmahat) \end{align}\]

into the negative log-likelihood. The result is called the negative profile log-likelihood

\[\begin{equation} -PLL= \frac{nd}{2}\log\left(2\pi\hat{\sigma}^2\right) +\frac{nd}{2} -n\sum_{j=1}^d\log f(b_j) +\frac{1}{2}\sum_{k=1}^n\sum_{j=1}^d \log\sqrt{1+Y_{kj}^2}. (\#eq:npll) \end{equation}\]

Note that this no longer decomposes into a sum of terms for each \(j\) that are independent of each other – the terms for different \(j\) are coupled through Equations @ref(eq:muhat) and @ref(eq:sigmahat). We need the following derivatives.

\[\begin{align} \frac{\partial \hat{\sigma}^2}{\partial a_i} &= \frac{2}{nd}\sum_{k=1}^n r_{ki}\frac{\partial h(y_{ki})}{\partial a_i}\nonumber\\ &= \frac{2}{nd} \sum_{k=1}^n r_{ki}A_{ki}\\ \frac{\partial \hat{\sigma}^2}{\partial b_i} &= \frac{2}{nd}\cdot f'(b_i) \sum_{k=1}^n r_{ki}A_{ki}y_{ki} \end{align}\]

So, finally

\[\begin{align} \frac{\partial}{\partial a_i}(-PLL) &= \frac{nd}{2\hat{\sigma}^2}\cdot \frac{\partial \hat{\sigma}^2}{\partial a_i} +\sum_{k=1}^n A_{ki}^2Y_{ki}\nonumber\\ &=\sum_{k=1}^n \left(\frac{r_{ki}}{\hat{\sigma}^2}+A_{ki}Y_{ki}\right)A_{ki} (\#eq:ddanpll)\\ \frac{\partial}{\partial b_i}(-PLL) &= -n\frac{f'(b_i)}{f(b_i)} + f'(b_i) \sum_{k=1}^n \left(\frac{r_{ki}}{\hat{\sigma}^2}+A_{ki}Y_{ki}\right)A_{ki}y_{ki} (\#eq:ddbnpll) \end{align}\]

## Summary

Likelihoods, from Equations @ref(eq:nll) and @ref(eq:npll):

\[\begin{align} -LL_i&= \underbrace{% \frac{n}{2}\log\left(2\pi\sigma^2\right) }_{\mbox{scale}} + \underbrace{% \sum_{k=1}^n \frac{(h(y_{ki})-\mu_k)^2}{2\sigma^2} }_{\mbox{residuals}} \underbrace{% -n\log f(b_i) + \frac{1}{2}\sum_{k=1}^n \log(1+Y_{ki}^2) }_{\mbox{jacobian}}\\ -PLL&= \underbrace{% \frac{nd}{2}\log\left(2\pi\hat{\sigma}^2\right) }_{\mbox{scale}}+ \underbrace{% \frac{nd}{2} }_{\mbox{residuals}} + \underbrace{% \sum_{i=1}^d\left( -n\log f(b_i) + \frac{1}{2}\sum_{k=1}^n \log(1+Y_{ki}^2)\right) }_{\mbox{jacobian}} \end{align}\]

The computations in the C code are organised into steps for computing the terms “scale”, “residuals” and “jacobian”.

Partial derivatives with respect to \(a_i\), from Equations @ref(eq:ddanll) and @ref(eq:ddanpll):

\[\begin{align} \frac{\partial}{\partial a_i}(-LL_i) &= \sum_{k=1}^n \left(\frac{r_{ki}}{\sigma^2}+A_{ki}Y_{ki}\right)A_{ki}\\ % \frac{\partial}{\partial a_i}(-PLL) &= \sum_{k=1}^n \left(\frac{r_{ki}}{\hat{\sigma}^2}+A_{ki}Y_{ki}\right)A_{ki} \end{align}\]

Partial derivatives with respect to \(b_i\), from Equations @ref(eq:ddbnll) and @ref(eq:ddbnpll):

\[\begin{align} \frac{\partial}{\partial b_i}(-LL_i) &= -n\frac{f'(b_i)}{f(b_i)} +f'(b_i)\sum_{k=1}^n \left(\frac{r_{ki}}{\sigma^2}+A_{ki}Y_{ki}\right)A_{ki}y_{ki}\\ % \frac{\partial}{\partial b_i}(-PLL) &= -n\frac{f'(b_i)}{f(b_i)} +f'(b_i)\sum_{k=1}^n \left(\frac{r_{ki}}{\hat{\sigma}^2}+A_{ki}Y_{ki}\right)A_{ki}y_{ki}. \end{align}\]

Note that the terms have many similarities – this is used in the implementation in the C code.

## References

Huber, Wolfgang, Anja von Heydebreck, Holger Sültmann, Annemarie Poustka, and Martin Vingron. 2002. “Variance Stabilization Applied to Microarray Data Calibration and to the Quantification of Differential Expression.” Bioinformatics 18 Suppl 1: 96–104. http://bioinformatics.oxfordjournals.org/content/18/suppl_1/S96.abstract.
———. 2003. “Parameter Estimation for the Calibration and Variance Stabilization of Microarray Data.” Statistical Applications in Genetics and Molecular Biology 2 (1): Article 3. http://www.degruyter.com/view/j/sagmb.2003.2.1/sagmb.2003.2.1.1008/sagmb.2003.2.1.1008.xml.