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\title{The PDNN model for the \Rpackage{affy} package}

\author{Laurent Gautier}
 
\begin{document}

\maketitle

\section{Introduction}

This package is our implementation of the PDNN model\cite{Zhang_etal}.
Whenever you use it, you can aknowledge it by quoting our published
work:
<<label=citation>>=
citation(package="affypdnn")
@ 

This package is also briefly described in Chapter `Preprocessing
High-density Oligonucleotide Arrays' of the Bioconductor book.


The first thing to do is to attach the package 
to the current R session.

<<label=init, results=hide>>=
library(affypdnn)
@ 

Upon executing this command, the package and its
dependencies will be attached (which will cause few
lines of text to appear on the console for your R session). 

Throughout this presentation of the package, we will
use `Dilution' dataset. The reader can replace it
with an arbitrary instance of class \Rclass{AffyBatch}.
<<label=afbatch>>=
library(affydata)
data(Dilution)
afbatch <- Dilution
@ 

We decomposed the model of Zhang {\it et al.} in such
a way that it fits the simple framework for probe-level
data processing implemented in the \Rpackage{affy} package:
\begin{enumerate}
\item Chip type-specific parameters are computed
\item Experiment-specific parameters are computed
\item Transform the PM probe signal using the PDNN model
(two flavours: `pdnn' and `pdnnpredict').
\item Compute probeset-level expression indexes
\end{enumerate}


\section{Chip type-specific parameters}

Chip type-specific parameters for \emph{U95Av2} are included with the package,
mostly to make some examples shorter to run:

<<label=hgu95av2Params>>=
data(hgu95av2.pdnn.params)

params.chiptype <- hgu95av2.pdnn.params
@ 

Here we are showing how to compute them.

Currently one needs an external data file, called `energy data file'.
This file contains parameters for all possible 16 dinucleotides
($E_g$ and $E_n$), as well as other parameters ($W_g$ and $W_n$).

These files were downloaded, and there is currently no implementation
to compute these data in this R package.
The files included within the package are:
<<label=energyFiles>>=
dir(system.file("exampleData", package="affypdnn"))
@ 

The Dilution dataset is of chip-type \emph{HGU95Av2}.
We read the `energy data file', then compute the parameters
(note that the probe package is needed):

\begin{Scode}
energy.file <- system.file("exampleData", 
                           "pdnn-energy-parameter_hg-u95av2.txt", 
                           package = "affypdnn")

params.chiptype <- pdnn.params.chiptype(energy.file, 
                                        probes.pack = "hgu95av2probe")
\end{Scode}



\section{Experiment-specific parameters}

Parameters specific to an experiement, that is the probe-level values
in a CEL file, can be computed easily:

<<label=experimentSpecific, echo=FALSE>>=
data("params.dilution", package="affypdnn")
params <- params.dilution
rm(params.dilution)
@ 

\begin{Scode}
params <- find.params.pdnn(afbatch, hgu95av2.pdnn.params)
\end{Scode}


\section{Transform the PM probe-level signal}

Here we arbitrarily pick two probesets:
<<label=pmLevel>>=
ppset.name <- c("41206_r_at", "31620_at")
ppset <- probeset(afbatch, ppset.name)
@ 

Computing the transformed the PM probe-level signals
is then just a matter of calling one of the functions:
\begin{itemize}
  \item \Rfunction{pmcorrect.pdnnpredict}
  \item \Rfunction{pmcorrect.pdnn}
\end{itemize}

<<label=pmLevelPlot, fig=TRUE>>=
par(mfrow=c(2,2))
for (i in 1:2) {
  probes.pdnn <- pmcorrect.pdnnpredict(ppset[[i]], params,
                                       params.chiptype = params.chiptype)

  plot(ppset[[i]], main=paste(ppset.name[i], "\n(raw intensities)"))
  matplotProbesPDNN(probes.pdnn, 
                    main = paste(ppset.name[i], 
                      "\n(predicted intensities)"))
}
@ 


\section{\Rfunction{expressopdnn}}
Processing probe-level data can be done by using a
modified\footnote{The design of the function \Rfunction{expresso}
showed its limitations with the requirements for this one package,
and a slightly modified version had to be written.} version
of the function \Rfunction{expresso} in the \Rpackage{affy} package.

Like its \Rpackage{affy} counterpart, \Rfunction{expressopdnn}
is a simple wrapper around a sequence of preprocessing steps.
The example below shows a typical usage of it; the documentation page
can be referred to for an exhaustive
description of the parameters it accepts.

Here we take only ten probesets:
<<label=expressopdnn>>=
ids <- ls(getCdfInfo(afbatch))[1:10]
eset <- expressopdnn(afbatch, 
                     bg.correct = FALSE,
                     normalize = FALSE,
                     findparams.param = list(params.chiptype = params.chiptype,
                       give.warnings=FALSE),
                     summary.subset=ids)
@ 
One can note that we leave background correction and normalization aside,
but it is obviously possible to mix-and-match with any such method available.

\end{document}
