%\VignetteIndexEntry{codelink}
%\VignetteKeywords{Preprocessing, Codelink}
%\VignetteDepends{codelink}
%\VignettePackage{codelink}

\documentclass{article}
\usepackage[latin1]{inputenc}
\usepackage[english]{babel}

\newcommand{\Robject}[1]{{\texttt{#1}}}
\newcommand{\Rfunction}[1]{{\texttt{#1}}}
\newcommand{\Rpackage}[1]{{\textit{#1}}}

\begin{document}

\title{Codelink}

\author{Diego Díez Ruiz}

\maketitle

\section{Introduction}

Codelink is a platform  for the analysis of gene expression on biological 
samples property of Applied Microarrays, Inc. (previously was GE Healthcare and 
Amersham). The hybridization reagents are still supplied by GE Healthcare.

The system uses 30 base long oligonucleotide probes for expression testing.  
There is a proprietary software for reading scanned images, doing spot 
intensity quantization and some diagnostics. The software assigns quality flags 
(see Table~\ref{tab:Flag}) to each spot on the basis of a signal to noise ratio 
(SNR) computation (Eq: \ref{eq:SNR}) and other morphological characteristics as 
irregular shape of the spots, saturation of the signal or manufacturer spots 
removed. By default, the software performs background correction (subtract) 
followed by median normalization. The results can be exported in several 
formats as XML, Excel, plain text, etc. This library allows to read Codelink 
plain text exported data into R \cite{r} for the analysis of gene expression 
with any of the available tools in R+Bioconductor\cite{BIOC}.

For storing the available data, a new class \Robject{Codelink} was designed.
The now defunt \Robject{exprsSet} in the \Rpackage{Biobase} package was not a 
convenient store class for Codelink data, because it didn't allow probes with 
duplicated names, as those names are stored as rownames in the expression 
matrix.

Currently there is experimental support for a \Robject{ExpressionSet} derived 
class that can accomodate duplicated probe names in an 
\Robject{AnnotatedDataFrame} whereas the feature ids would be used for row 
names when available.

\begin{table}[ht]
\begin{center}
\begin{tabular}{|c|l|}
\hline \em Flag & \em Description \\
\hline
G & Good signal (SNR >= 1)\\
L & Limit signal (SNR < 1)\\
I & Irregular shape \\
S & Saturated signal \\
M & MSR spot \\
C & Background contaminated\\
X & User excluded spots\\
\hline
\end{tabular}
\caption{Quality Flag description. SNR: Signal to Noise Ratio.}
\label{tab:Flag}
\end{center}
\end{table}

\begin{equation}
SNR=\frac{Smean}{(Bmedian + 1.5 * Bstdev)}
\label{eq:SNR}
\end{equation}

\begin{table}[ht]
\begin{center}
\begin{tabular}{|r|l|}
\hline \em Probe type & \em Description \\
\hline
DISCOVERY & Gene expression testing probes \\
POSITIVE & Positive control probes \\
NEGATIVE & Negative control probes \\
FIDUCIAL & Grid alignment probes \\
OTHER & Other controls and housekeeping gene probes \\
\hline
\end{tabular}
\caption{Probe types for Codelink arrays.}
\label{tab:Type}
\end{center}
\end{table}

\section{Reading data}

Currently only data exported as plain text from Codelink software is supported.  
Unfortunately the Codelink exported text format can have arbitrary columns and 
header fields so depending of what you have exported you can read it or not.  
The suggestion is that you put on the files everything you can, including  
Spot\_mean and Bkgd\_median values so you can do background correction and 
normalization in R. In addition, Bkgd\_stdev is needed to compute the SNR. If 
you put Raw\_intensity or Normalized\_intensity columns then you can also read 
it directly and avoid background correction and/or normalization but this is 
not recommended. To read some Codelink files you do:

\begin{Sinput}

# NOT RUN #

> library(codelink)
> foo <- readCodelink()
> summaryFlag(foo) # will show a summary of flag values.

# NOT RUN #

\end{Sinput}

This suppose that your files have the extension ``TXT'' (uppercase)
and that they are in your working directory. If this is not the case you can 
specify the files to be read with the 'file' argument. The function 
\Rfunction{readCodelink} returns and object of \Robject{Codelink} similar to 
that:

<<data>>=

library(codelink)

data(codelink.example)

codelink.example

@

\begin{table}[ht]
\begin{center}
\begin{tabular}{|r|l|}
\hline \em Slot & \em Description \\
\hline
product & Chip name description \\
sample & Sample names vector \\
file & File names vector \\
name & Probe names vector \\
type & Probe types vector \\
method & Methods applied to data \\
method\$background & Background correction method used \\
method\$normalization & Normalization method used \\
method\$merge & Merge method used \\
method\$log & Logical: If data is in log scale \\
flag & Quality flag matrix \\
Smean & Mean signal intensity matrix \\
Bmedian & Median background intensity matrix \\
Ri & Raw intensity matrix \\
Ni & Normalized intensity matrix \\
snr & Signal to Noise Ratio matrix \\
cv & Coefficient of Variation matrix \\
\hline
\end{tabular}
\caption{Description of Codelink object slots.}
\label{tab:Slots}
\end{center}
\end{table}

The \Robject{Codelink-class} is basically a list that stores information in 
several slots (see Table~\ref{tab:Slots}). The chip type (product slot) is read 
from the PRODUCT field if found in the header of Codelink files. If it is not 
found then a warning message is shown and product slot is set to "Unknown". If 
the product is not the same in all the files the reading is canceled with an 
error message.

By default, all spots flagged with M, I, and S flags are set to NA. This can be
controlled with the flag argument of \Rfunction{readCodelink}. The flag 
argument is a list that can contain a valid flag identifier and a value for 
that flag.  For example, if you want to set all M flagged spots to 0.01 and let 
other spot untouched you do:

\begin{Sinput}
# NOT RUN #

> foo <- readCodelink(flag = list(M = 0.01) )

# NOT RUN #
\end{Sinput}

It is possible to find probes wit more that one flag assigned, i.e. CL for a 
probe labeled as C and L, CLI for a probe labeled as C, L and I, and so on. A a 
regular expression is used to find flag types in an attempt to to manage all 
the possible situations. When two user modified flags fall in the same probe 
the more restricting (NA being the most) is assigned.

\section{Background correction}

If you have Spot\_mean values Bkgd\_median values the you can apply one of the 
several background correction methos interfaced. This is done by the function 
\Rfunction{bkgdCorrect}. To see the different options look at ?bkgdCorrect. For 
instance, if you want to apply \emph{half} method you do:

\begin{Sinput}

> foo <- bkgdCorrect(foo, method = "half")
 
\end{Sinput}

The default method used is \emph{half} and is based in the same method applied 
in the \Rpackage{limma} \cite{limma} package to two channel microarrays. In 
this method, the median background intensity (Bmedian) is subtracted from mean 
spot intensity (Smean) and any value smaller that 0.5 is shift to 0.5 to ensure 
no negative numbers are obtained that would prevent to transform the data into 
log scale. Other available methods are \emph{none} that let the spot 
intensities untouched, \emph{subtract} that is analog to the default method 
used in the Codelink software and \emph{normexp} and interface to the method 
available in the \Rpackage{limma} package.

\section{Normalization}

Normalization of the background corrected intensities is done by the wrapper 
function \Rfunction{normalize}. The default method is \emph{quantile} 
normalization that in fact call \Rfunction{normalizeQuantiles()} from 
\Rpackage{limma} package (allowing for NAs). There is also the possibility to 
use a modified version of CyclicLoess from \Rpackage{affy} \cite{affy} package 
that allow using weights and missing values. Finally, the \emph{median} 
normalization allows to normalize using a method analog to the default method 
in the Codelink software. To normalize you usually do:

\begin{Sinput}

> foo <- normalize(foo, method = "quantiles")

\end{Sinput}

By default, \Rfunction{normalize} return log2 intensity values.  This could be
controlled setting the parameter log.it to FALSE.

\section{Plotting}

There are some diagnostic plots available for the \Robject{Codelink} object.  
These are functions for producing MA plots (\Rfunction{plotMA}), scatterplots 
(\Rfunction{plotCorrelation}) and density plots (\Rfunction{plotDensities}).  
All functions use the available intensity value (i.e.  Smean, Ri or Ni) to make 
the plot.

The functions \Rfunction{plotMA} and \Rfunction{plotCorrelation} can highlight 
points based on the Spot Type, which is the default behavior or using the SNR 
values.  The mode is controlled with argument \emph{label}.  
\Rfunction{plotCorrelation} requires arguments \emph{array1} and \emph{array2} 
to be set in order to select which arrays are going to be plotted. For 
\Rfunction{plotMA} if only \emph{array1} is specified, the values are plotted 
againts a pseudoarray constructed with the mean of the probe intensities along 
all available arrays. M and A values are computed following equations 
\ref{eq:M} and \ref{eq:A}.

\begin{center}

<<plotMA, fig=TRUE>>=

plotMA(codelink.example)

@
\end{center}

\begin{equation}
M=Array2-Array1
\label{eq:M}
\end{equation}

\begin{equation}
A=\frac{Array2+Array1}{2}
\label{eq:A}
\end{equation}

The function \Rfunction{plotDensities} plot the density of intensity values of
all arrays. It can plot only a subset of arrays if the \emph{subset} argument 
is supplied.

\begin{center}
<<plotDensity, fig=TRUE>>=

plotDensities(codelink.example)

@
\end{center}

When Logical\_row and Logical\_col columns are exported into they are stored 
into the \emph{logical} slot. This information stores the physical location of 
each probe in the array, and can be used to plot pseudo images of the array 
intensities. To plot a pseudo image you should use:

\begin{Sinput}

> imageCodelink(foo)
> imageCodelink(foo, what = "snr")

\end{Sinput}

It is possible to plot the background intensities (default), the spot mean, 
raw and normalized intensities and the SNR values. This images are useful to 
identify spatial artifact that may be affecting the analysis.

\section{Miscellaneous}

There are also some miscellaneous functions used in some analysis that could be
useful for someone.

\subsection{Using weights}

The \Rfunction{createWeights} function creates a matrix of weights based on 
probe type labels to be used, for example, when fitting a linear model with 
\Rpackage{limma} \cite{limma}.

<<createWeights>>=

w <- createWeights(codelink.example, type = list(FIDUCIAL = 0.01, NEGATIVE = 
0.1))

w[1:10,]

@

\subsection{Merging arrays}

In case you want to merge array intensities the \Rfunction{mergeArray} function 
help on this task. It computes the mean of Ni values on arrays of the same 
class.  The grouping is done by means of the \emph{class} argument (numerical 
vector of classes). New sample names should be assigned to the sample slot 
using the \emph{names} argument. The function also returns the coefficient of 
variation in the \emph{cv} slot. The distribution of coefficients of variations 
can be checked with the function \Rfunction{plotCV}.

\begin{Sinput}

> foo <- mergeArray(foo, class = c(1, 1, 2, 2),
+ names = c("A", "B"))

> plotCV(foo)

\end{Sinput}

\section{Problems reading data}
Some updated version of the Codelink software changed the order in which probes 
are printed in the exported text files. That makes files from these different 
software version impossible to be analyzed together. The function 
\Rfunction{readCodelink} have a new argument \emph{check} TRUE by default, that 
check the Probe\_name columns to see if they have the same order in all the 
arrays at the cost is a little extra loading time. This behaviour can be turn 
off by setting \emph{check} to FALSE. If an ordering problem is found, a 
warning message is print but the reading of data is not stopped, allowing the 
visual examination of the data. In this case, the best option is to export the 
old files again using the updated version of the software. If this is 
impossible for whatever reason, the codelink package can try to fix the order 
of the files.

To the aim, a new argument \emph{fix = TRUE} can be passed to 
\Rfunction{readCodelink}. The method will try to order the probes using the 
Feature\_id column, which is a combination of Logical\_row and Logical\_col and 
for this, a unique identifier of each probe. This is the optimal fix, and the 
new data should be perfectly ordered. If that column is missing, the it will 
try to order the data using the Probe\_name column. This is a sub-optimal 
solution because as the fiducial, control and some discovery probes have 
duplicated Probe\_name, they may be end messed up. In this case, the best 
solution again, is to try to export again the data with the updated version of 
the software.


\begin{Sinput}

> foo <- readCodelink(fix = TRUE)

\end{Sinput}

\section{Future improvements}
As the new classes in the Biobase package (\Robject{eSet} and 
\Robject{ExpressionSet}) allow more flexible data structures, a 
reimplementation of the \Robject{Codelink-class} based on 
\Robject{ExpressionSet-class} will be the next major feature added to this 
package. This will allow a better integration with other tools available 
through the Bioconductor project.

Right now, there is some experimental implementation of the new codebase. You 
need to use the wrapper function \Rfunction{readCodelink2}.

\begin{Sinput}

> foo <- readCodelink2()

\end{Sinput}


\bibliographystyle{plain}
\bibliography{codelink}


\end{document}
