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\begin{document}

\title{The AffyRNADegradation Package}
\author{Mario Fasold}
\maketitle

Affymetrix 3' expression arrays employ a specific experimental
protocol and a specific probe design that allows assessment of RNA integrity
based on probe signal data. Problems of RNA integrity are primarily
governed to the degradation of the target transcripts. It can be shown
that
%\begin{enumerate}
\begin{enumerate}[(i)]%for capital roman numbers.
\item degradation leads to a probe positional bias that needs to be
  corrected in order to compare expression of samples with varying
  degree of degradation, and
\item it is possible to estimate a robust and accurate measure of RNA
  integrity from the probe signals that, for example, can be used to study
  degradation within the large number of available microarray data.
\end{enumerate}
The rationale and further analysis are described in the accompanying
publication by Fasold and Binder. We here show how to utilize this
package for both problems.

\section{Basic RNA Degradation Analysis}

We here show how to use the package for the analysis of RNA
degradation. Let us first load exemplar data provided by the
\Rpackage{AmpAffyExample} package into the environment.
<<>>=
library(AffyRNADegradation)
library(AmpAffyExample)
data(AmpData)
AmpData
@
 
Every transcript is measured by a set of 11-16 probes. The log-average
intensity difference between probes located closer to the 3' end of
the target transcripts and those located further away constitutes the 
probe positional bias. It can be visualized using the {\it tongs plot}.
<<label=plotTongs,include=FALSE>>=
tongs <- GetTongs(AmpData, chip.idx = 4) 
PlotTongs(tongs)
@
\begin{figure}
\begin{center}
<<label=plotTongs,fig=TRUE,echo=FALSE>>=
<<plotTongs>>
@
\end{center}
\caption{The tongs plot shows that the intensity difference between 3'
  and 5' probes increases with $\Sigma=\langle \log I
  \rangle$. $\langle \rangle$ here denotes either averaging over all probes within
  the probeset, or averaging over the 3' or 5' subset of probes in $\Sigma_{subset}$.}
\label{fig:tongs}
\end{figure}
Figure~\ref{fig:tongs} shows that the bias relates to the expression
level of the transcripts. As this can vary from sample to sample,
it must be considered in estimating of RNA degradation. 

The function \Rfunction{RNADegradation} performs the basal analysis of RNA
degradation based on raw probe intensities stored in an AffyBatch
object. The result is an \Robject{AffyDegradationBatch} object that contains the
corrected probe intensities as well as several statistical parameters.
<<correctAffy>>=
rna.deg <- RNADegradation(AmpData, location.type = "index")
@

We can visualize the probe positional bias using the \Rfunction{PlotDx} function.
<<label=plotDx,include=FALSE>>=
plotDx(rna.deg)
@
\begin{figure}
\begin{center}
<<label=fig1,fig=TRUE,echo=FALSE>>=
<<plotDx>>
@
\end{center}
\caption{Probe degradation plot. The points show the average probe
  intensity of expressed genes for each index $x=1,..11$ relative to
  the average intensity at position $x=1$. The lines are a
  fitted decay function.}
\label{fig:one}
\end{figure}
Figure~\ref{fig:one} shows the results. Different degradation
between different samples are observed. 

To access the parameter $d$, which provides a robust, sample-wise measure for the
degree of RNA degradation, one can use the function
<<RnaIntegrityD>>=
d(rna.deg)
@

\section{Absolute Probe Locations}

Instead of using the probe index within the probeset as argument of
the degradation degree, one can use the actual probe locations within
the transcript. We have pre-computed the distance of each probe to the
3' end of its target transcript for all Affymetrix 3' expression
arrays. These probe location files are available under the URL
\url{http://www.izbi.uni-leipzig.de/downloads_links/programs/rna_integrity.php}.

In order to perform the analysis and correction using absolute
probe locations, one must first download the probe location file for
the used chip type. You can then start the analysis using
\Rfunction{RNADegradation}, as above, but selecting
\texttt{absolute} as \texttt{location.type}.  The parameter
\texttt{location.file.dir} must specify the download directory of the
probe location file. 

% <<correctAffyAbsolute, results=hide, eval=FALSE>>=
% # do not run as additional file needed
% rna.deg = RNADegradation(AmpData, location.type = "absolute", location.file.dir = "[SOME_DIR]")
% @ 

\section{Correction of the Bias and Integration into the Microarray Calibration Process}

The correction of the probe positional bias is performed within the
\Rfunction{Affy\-RNA\-Degradation} function. The result is a new
\Robject{AffyBatch} object with corrected probe level intensities. It
can be accessed using the \Rfunction{afbatch} function
<<correctAffy, results=hide>>=
afbatch(rna.deg)
@ 
It is possible to replace the original raw data with this data
corrected for probe positional bias, before performing further
microarray normalization and summarization (e.g. using RMA).

Alternatively, correction can be performed after probe-level
normalization in a microarray calibration pipeline. The following
example shows how to first apply the VSN normalization method, then
correct for probe positional bias to finally get summarized expression
measures
<<vsnCorrect, results=hide, eval = FALSE>>=
library(vsn)
affydata.vsn <- do.call(affy:::normalize, c(alist(AmpData, "vsn"), NULL))
affydata.vsn <- afbatch(RNADegradation(affydata.vsn))
expr <- computeExprSet(affydata.vsn, summary.method="medianpolish", pmcorrect.method="pmonly")
@

\section{Details}
This document was written using:

<<details>>=
sessionInfo()
@

\end{document}