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\documentclass[12pt]{article}

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\begin{document}
\title{The analysis of rtPCR data}
\author{Jitao David Zhang, Markus Ruschhaupt}
\maketitle

With the help of this document, an analysis of rtPCR data can be
performed. For this, the user has to specify several parameters described
below in the '.Rnw' version of this document. After that the software R can be
used for the analysis by processing the '.Rnw' version. In the beginning you will find a
short description of the calculated values. After that the parameters that
have to be specified are explained. 

\section{The calculated values}

Several values are calculated during the execution of this document. Some of
them are explained here. The values explained here are calculated if no efficiencies are used (see below). 

\begin{enumerate}
\item{The mean or median of the replicates for one gene/sample combination is the Ct value.}
\item{For a sample $A$ the Ct value of the housekeeping gene (or the median of the Ct values of
all housekeeping genes) is subtracted from the corresponding Ct value of a gene $Gen1$. The result is the dCt value for $Gen1$ and sample $A$. This value is used for the t-test and the
Wilcoxon test.}
\item{For a gene $Gen1$ the dCt value of the reference sample (or the mean of the dCt values of
all reference samples) is subtracted from the corresponding  dCt value of
$Gen1$ and a sample $B$. This is called the ddCt value for $Gen1$ and sample $B$.}
\item{The transformation $x \rightarrow 2^{-x}$ is applied to each ddCt
value. The resulting value is called 'level'.}
\end{enumerate}
%
Additionally, for each value an error is calculated. This is based on the
standard error of the mean if the mean is used to summarize the triplets. If
the median is used for summarization of the individual Ct values, the MAD divided by the square root of all samples is
used as an error estimate.



<<setup,echo=FALSE,results=hide>>= 
options(width=50)
@

\section{Preparation}

The following programs and packages have to be installed on the system. 
\begin{itemize}
\item R (at least version 2.2.0)
\item R package Biobase (at least version 1.7)
\item R package RColorBrewer
\item R package ddCt
\item R package lattice
\item R package xtable
\end{itemize}


In case of using this manuscript first time, you have to install the
\Rpackage{ddCt} package. This process can be performed
semi-automatically by copying the codes of the following section
(titled \emph{ddCtInstall}), which will download the current version
of \Rpackage{ddCt} (dependent on the version of your R program) and
install it.

<<ddCtInstall, eval=FALSE, echo=TRUE>>=
source("http://www.bioconductor.org/biocLite.R")
biocLite(ddCt)
@ 

To run this document and perform
the analysis you need a tab-delimited text file containing the individual Ct values of your experiment. This file
can be exported from the software used to measure the Ct values. It is
important that you export the individual Ct values (normally the software
combines the replicates of the measurements to one value). After you have
done this, you can follow this instruction step by step and set the appropriate parameters.


\section{Choice of the parameters}

First you have to specify the directory where your data is stored (load.path) and the
directory where the results are supposed to be stored (save.path). You can specify the
path relatively to the directory you are in when starting the script in R. 

<<specifyFiles,results=hide>>=
load.path = "."
save.path = "."
@

Then you have to specify the name of the file containing the individual Ct values
(Ct.data.file.name). Additionally, you may specify a file containing
additional data related to the samples (sample.annotation.file.name). 
This should be a tab-delimited text file with several columns and one row for
each sample. Have a look at the example 'SampleAnno.txt'. The first column
has to include the sample names and has to be named 'Sample'. 

A file with additional information is important if you
want to perform a t-test, if you use special colors for groups of samples, or if you want the samples to be grouped according
to one column of the sample annotation file that can be specified through the parameter
column.for.grouping. If all of this is not important to you, just
set all parameters to 'NULL'. This also holds for other parameters described
below that may be specifies but do not have to. If you do not want to specify
such a parameter, just put 'NULL' there. If you have a sample annotation file
and want to include only samples from this file into your final object, you
can set the parameter 'useOnlySamplesFromAnno'.

<<The Data>>=

Ct.data.file.name       <-  c("")

sample.annotation.file.name  <- NULL
column.for.grouping          <- NULL
useOnlySamplesFromAnno       <- FALSE
@

In case no input file was specified, a default file will be used for the demonstrative purpose.

%--------------------------------
\subsection*{Change gene names and sample names}
%--------------------------------

You may change gene names and sample names, which is 
important for the final plot of your data. If you change gene/sample names you
also change the ordering of the genes/samples in your plot.

In this example, 'Gen1' will become 'Gene A', 'Gen4' will become 'Gene B', and
so on. In the final plot, Gene A will be plotted first, then Gene B followed by
Gene C, HK1 and HK2. If you want to rename genes, {\bf all} genes have to be
included into this list. You cannot exclude genes that you do not want to be renamed.
The sample names may be changed in the same way. 

<<transform>>=
#new.gene.names    <- c("Gen1"="Gene A",
#		        "Gen4"="Gene B",
#			"Gen5"="Gene C",
#			"Gen2"="HK1",
#			"Gen3"="HK2")
new.gene.names    <- NULL
new.sample.names  <- NULL
@

\noindent {\bf Attention:} If you rename genes or samples, the new names must be
used for the parameters further down below. 

%--------------------------------
\subsection*{Housekeeping genes and reference samples}
%--------------------------------

Here you have to specify the housekeeping gene(s) and the reference
sample(s). In this example, one reference sample and two housekeeping
genes are used. If more than one object is specified, the names have to be given as
shown in this example.
If you specify more than one housekeeping gene, the algorithm will use the mean
of the Ct values of the housekeeping genes for normalization. If you use more
than one sample, the algorithm uses the mean of the chosen samples as the
reference line. 

<<housekeeping>>=
name.referenz.sample <- c("Sample2")
name.referenz.gene   <- c("Gene3")
@


You may set a threshold for the Ct values. All individual Ct values values above this
threshold will be treated as 'undetermined'. 
<<threshold>>=
Threshold.for.Ct <- 40
@

Here you have to specify if you want to use the 'mean' or the 'median' for merging the individual Ct values for a gene/sample combination.
The median is often more appropriate because it is more robust.
<<mean/median>>=
TYPEOFCALCULATION <- "mean"
@


%--------------------------------
\subsection*{Efficiencies}
%--------------------------------

\noindent You may include efficiencies for each gene. There is also the possibility to include error estimates for the
efficiencies (for example a standard deviation). These estimates will be used for the
error calculation. These efficiencies can be specified in the following way.


<<eff, eval=FALSE>>=
EFFICIENCIES <- c("Gene A"=1.9,"Gene B"=1.8,"HK1"=2,"Gene C"=2,"HK2"=2)
EFFICIENCIES.ERROR <- c("Gene A"=0.01,"Gene B"=0.1,"HK1"=0.05,"Gene C"=0.01,"HK2"=0.2)
@

\noindent If you use efficiencies, only the raw Ct value and the final 'level' is
calculated. There are no 'dCt' or 'ddCt' values. Hence no t-test can
be performed if efficiencies are used. In this example we do not use efficiencies.

<<effizienzen>>=
EFFICIENCIES <- NULL
@

<<eff.error>>=
EFFICIENCIES.ERROR <- NULL
@


%--------------------------------
\subsection*{Plot parameter}
%--------------------------------

The following parameters are used to change the final plot. First you have to
specify what you want to plot. Here you can specify any or both of the
following two choices:
\begin{itemize}
\item level - For each gene and sample the relative expression to the reference
line is plotted
\item Ct - the raw, unnormalized but merged Ct values are plotted
\end{itemize}

<<PlotKind>>=
TheKindOfPlot <- c("level","Ct")
@


Sometimes genes and/or samples are not wanted to be included in the final plot. And
sometimes you only want to include a small fraction of genes and/or samples.
Have a look at the examples. We do not want the "NTC" sample to appear in
the plot:
%
% Example 1
% names.of.the.samples.REMAIN.in.Output <- c("293","ACHN","CAKI 1","CAKI2","Reference")
% Here only the samples "293","ACHN","CAKI 1","CAKI2","Reference" will be
% included in the final plot
%
%
% Example 2
%
% names.of.the.genes.REMAIN.in.Output   <- NULL
% names.of.the.genes.NOT.in.Output      <- c("Gen1","Gen3")
% Here only "Gen1" and "Gen3" will be removed from the final plot
%

<<remaining>>=
#REMAIN
names.of.the.genes.REMAIN.in.Output   <- NULL
names.of.the.samples.REMAIN.in.Output <- NULL
#NOT
names.of.the.genes.NOT.in.Output      <- NULL
names.of.the.samples.NOT.in.Output    <- NULL
@


Now you have to consider the following questions: 
Do you want the final plot to be drawn in a way such that the samples are plotted next to
each other ? 

<<grouping>>=
GROUPINGBYSAMPLES <- TRUE
@

\noindent And would you like each gene or sample to have its own
plot? 

<<plot0>>=
own.plot.for.each.object     <- TRUE
@


\noindent This is often useful if you have
many genes or samples. Depending on the parameter GROUPINGBYSAMPLES either
each gene (GROUPINGBYSAMPLES=TRUE) will have its own plot, or each sample 
( GROUPINGBYSAMPLES = FALSE) will have its own plot.


If you have a single plot for each individual gene, then you may color the
sample bars according to one parameter of your sample annotation file (if you
have specified such a file in the beginning of this script). You may also specify
the colors.

<<plot1>>=
GroupingForPlot <- NULL
GroupingColor   <- c("#E41A1C","#377EB8","#4DAF4A","#984EA3","#FF7F00")
@

You may specify a CUTTOFF for the y axis for all plots. Then all plots have the
same scale. 
<<plot2>>=
CUTOFF <- NULL
@

With the parameter BREWERCOL you can specify color sets you want to use in order to
color the individual bars. For additional information have a look at the
description of the 'RColorBrewer' package.

<<plot3>>=
BREWERCOL <- c("Set3","Set1","Accent","Dark2","Spectral","PuOr","BrBG")
@

Would you like to plot a legend? (TRUE/FALSE). Sometimes the
plot will be messed up if a legend is plotted, so please have a try.

<<plot4>>=
LEGENDE <- TRUE
@

%--------------------------------
\subsection*{t-test and Wilcoxon test  specification}
%--------------------------------

You may perform a t-test and a Wilcoxon test if you have specified a sample
annotation file above. {\bf Attention:} A t-test and a Wilcoxon test will not work if efficiencies are used for
the calculation.
<<ttest1>>=
perform.ttest <- FALSE
@


If you want to do a t-test/Wilcoxon test, you have to specify the name of the
column of your sample information file that includes the group information needed for the tests. If there are
more than two groups in this column, tests for each possible pair of groups
are performed.

<<ttest>>=
column.for.ttest <- NULL
@

If you want to perform a paired t-test/Wilcoxon test, you have to specify a column of your
sample information file that contains information describing the pairing of
the samples. A pair of samples must have the same number/parameter in that
column. Please have a look at the example.

<<ttest3>>=
column.for.pairs     <- NULL      ## ansonsten NULL
@

You can specify whether you want to exclude some samples 
from the t-test. Here we again want to exclude the 'NTC' sample.
<<ttest4>>=
names.of.the.samples.REMAIN.in.ttest <- names.of.the.samples.REMAIN.in.Output
names.of.the.samples.NOT.in.ttest    <- names.of.the.samples.NOT.in.Output

@



%------------------------------
\subsection*{Housekeeping gene correlation}
%------------------------------------------

If two or more housekeeping genes are used, the correlation (Pearson and
Spearman) for each pair of housekeeping genes is calculated. Additionally a
plot is produced. You may specify some samples that are not supposed to be used for
the correlation calculation, for example a no template control. In the default
setting those samples are excluded that are also excluded from the plot.

<<>>=
names.of.the.samples.REMAIN.in.cor <- names.of.the.samples.REMAIN.in.Output
names.of.the.samples.NOT.in.cor    <- names.of.the.samples.NOT.in.Output 
@


\noindent Now all parameters have been set. You have to start R, change the directory if you like, and then type the following:

Sweave("RT-PCR-Script-ddCt.Rnw")

%
%  ----------------------------------------------------------------------------
%

<<lib, echo=FALSE, results=hide>>=
library(ddCt)
@

<<runastest, echo=FALSE, results=hide>>=
## in case key parameters were not specified, the script runs in test mode
testMode <- FALSE
if (length(Ct.data.file.name) == 1 & Ct.data.file.name[1]=="")
  testMode <- TRUE
if(testMode) {
  Ct.data.file.name <- "Experiment1.txt"
  load.path <- system.file("extdata", package="ddCt")
  name.referenz.sample <- c("Sample2")
  name.referenz.gene   <- c("Gene3","Gene2")
}
@ 

<<readData,results=hide,echo=FALSE>>=
datadir <- function(x) file.path(load.path, x)
savedir <- function(x) file.path(save.path,x)
file.names <- datadir(Ct.data.file.name)
sho <- paste(gsub(".txt","",Ct.data.file.name),collapse="_")

warningFile <- savedir(paste("warning.output",sho,".txt",sep=""))

CtData <- SDMFrame(file.names)

if (!is.null(Threshold.for.Ct)){
 A <- Ct(CtData)>= Threshold.for.Ct
 coreData(CtData)$Ct[A] <- NA
}

if (! is.null(new.gene.names))   CtData[,2] <- new.gene.names[CtData[,2]]
if (! is.null(new.sample.names)) CtData[,1] <- new.sample.names[CtData[,1]]



if(! is.null(sample.annotation.file.name)){
   info <- datadir(sample.annotation.file.name)
   sampleInformation <- read.AnnotatedDataFrame(info,header=TRUE,sep="\t", row.names=NULL)
 }else{
   sampleInformation <- new("AnnotatedDataFrame",
                            data=data.frame(Sample=sampleNames(CtData)),
			    varMetadata=data.frame(labelDescription=c("unique identifier"),row.names=c("Sample")))
 }

 
 

if(useOnlySamplesFromAnno && !is.null(sample.annotation.file.name)){
  A <- CtData[,"Sample"] %in% pData(sampleInformation)[,"Sample"]
  warning( paste("Es werden folgende Samples entfernt:",paste(unique(CtData[!A,"Sample"]),collapse=", ")))
  CtData <- CtData[A,]
 }


if (is.null(EFFICIENCIES)){
  result <-      ddCtExpression(CtData,
                      calibrationSample=name.referenz.sample,
                      housekeepingGenes=name.referenz.gene,
                      type=TYPEOFCALCULATION,
                      sampleInformation=sampleInformation,
                      warningStream=warningFile)
  } else{
  result <- ddCtwithE(CtData,
                      calibrationSample=name.referenz.sample,
                      housekeepingGenes=name.referenz.gene,  
                      efficiencies=EFFICIENCIES,
                      efficiencies.error=EFFICIENCIES.ERROR,
                      type=TYPEOFCALCULATION,
                      sampleInformation=sampleInformation,
                      warningStream=warningFile)
  }
   
save(result,file=savedir(paste("Result",sho,".RData",sep="")))
@

<<write (HTML)Tables,echo=FALSE,results=hide>>=
htmlName   <- paste("HTML",sho,sep="_")
tablesName <- paste("Tables",sho,sep="_")
getDir <- function(dir, ...) {
  if(!file.exists(dir)) {
    dir.create(dir,...)
  }
  return(dir)
}


html.path <- getDir(file.path(save.path,htmlName))
table.path <- getDir(file.path(save.path, tablesName))

if(!is.null(sample.annotation.file.name) & !is.null(column.for.grouping))
 {result<- result[,order(pData(result)[,column.for.grouping])]}

elistWrite(result,file=savedir(paste("allValues",sho,".csv",sep="")))
EE1 <- assayData(result)$exprs
FF1 <- assayData(result)$level.err
if(!is.null(GroupingForPlot)) 
  GFP1 <- pData(result)[,GroupingForPlot]

Ct  <- round(assayData(result)$Ct,2)
lv  <- round(EE1,2)

write.table(cbind(t(EE1),t(FF1)),file=file.path(table.path,"LevelPlusError.txt"),sep="\t",col.names=NA)
write.table(lv,file=file.path(table.path,"level_matrix.txt"),sep = "\t", col.names = NA)
write.table(Ct,file=file.path(table.path,"Ct_matrix.txt"),sep="\t", col.names = NA)

write.htmltable(cbind(rownames(lv),lv),title="Level",file=file.path(html.path,"level"))
write.htmltable(cbind(rownames(Ct),Ct),title="Ct",file=file.path(html.path,"Ct"))


if(is.null(EFFICIENCIES)){
 dCtValues  <- round(assayData(result)$dCt,2)
 ddCtValues <- round(assayData(result)$ddCt,2)
 write.table(dCtValues,file=file.path(table.path,"dCt_matrix.txt"),  sep="\t", col.names=NA)
 write.table(ddCtValues,file=file.path(table.path,"ddCt_matrix.txt"),sep="\t",col.names=NA)

 write.htmltable(cbind(rownames(dCtValues),dCtValues)  ,title="dCt",file=file.path(html.path,"dCt"))
 write.htmltable(cbind(rownames(ddCtValues),ddCtValues),title="ddCt",file=file.path(html.path,"ddCt"))
}

@


<<plot Level,echo=FALSE,results=hide>>=
for(KindOfPlot in TheKindOfPlot){
if(KindOfPlot=="level"){
  EE1 <- assayData(result)$exprs
  FF1 <- assayData(result)$level.err
  theTitle <- "Level"
}

if(KindOfPlot=="ddCt"){
  EE1 <- assayData(result)$ddCt
  FF1 <- assayData(result)$ddCt.err
  theTitle <- "ddCt"
}

if(KindOfPlot=="dCt"){
  EE1 <- assayData(result)$dCt
  FF1 <- assayData(result)$dCt.err
  theTitle <- "dCt"
}

if(KindOfPlot=="Ct"){
  EE1 <- assayData(result)$Ct
  FF1 <- assayData(result)$Ct.err
  theTitle <- "Ct"
}



################
## order the set
################

if (!is.null(new.gene.names))
 {EE2  <- EE1[match(new.gene.names,rownames(EE1)),,drop=FALSE]
  FF2  <- FF1[match(new.gene.names,rownames(EE1)),,drop=FALSE]
 } else{
  EE2 <- EE1
  FF2 <- FF1
 }

if (!is.null(new.sample.names))
 {EE  <- EE2[,match(new.sample.names,colnames(EE2)),drop=FALSE]
  FF  <- FF2[,match(new.sample.names,colnames(EE2)),drop=FALSE]
  if(!is.null(GroupingForPlot)) GFP<- GFP1[match(new.sample.names,colnames(EE2))]
 } else{
  EE <- EE2
  FF <- FF2
  if(!is.null(GroupingForPlot)) GFP<- GFP1
 }

###################
## Reducing the set
###################


if (!is.null(names.of.the.genes.REMAIN.in.Output)){
   Gred <- (rownames(EE) %in% names.of.the.genes.REMAIN.in.Output)
}else{
   Gred <- !(rownames(EE) %in% names.of.the.genes.NOT.in.Output)
}


if (!is.null(names.of.the.samples.REMAIN.in.Output)){
   Sred <- (colnames(EE) %in% names.of.the.samples.REMAIN.in.Output)
}else{
   Sred <- !(colnames(EE) %in% names.of.the.samples.NOT.in.Output)
}

EEN <- EE[Gred,Sred,drop=FALSE]
FFN <- FF[Gred,Sred,drop=FALSE]
if(!is.null(GroupingForPlot)) GFPN <- as.factor(as.character(GFP[Sred]))

############
# the color
############

COLORS <- c()
for (i in 1:length(BREWERCOL))
  COLORS <- c(COLORS,brewer.pal(brewer.pal.info[BREWERCOL[i],]$maxcolors,BREWERCOL[i]))
if(GROUPINGBYSAMPLES){
  THECO  <- COLORS[1:sum(Sred)]
} else {
  THECO  <- COLORS[1:sum(Gred)]
}

##########
# plotting
##########
getFileName <- function(prefix="Ct", name) {
  sprintf("%s_Result_%s.pdf", prefix, sho)
}
if(own.plot.for.each.object){
 pdf(w=15,h=15,file=savedir(paste(theTitle,"Result",sho,".pdf",sep="")))
if(GROUPINGBYSAMPLES){
  for(k in 1:dim(EEN)[1]){
    EENN <- EEN[k,,drop=FALSE]
    FFNN <- FFN[k,,drop=FALSE]
    barploterrbar(EENN,EENN-FFNN,EENN+FFNN,barcol=THECO,legend=LEGENDE,columnForDiffBars=GROUPINGBYSAMPLES,theCut=CUTOFF,ylab=theTitle)
  }}else{
    for(k in 1:dim(EEN)[2]){
      EENN <- EEN[,k,drop=FALSE]
      FFNN <- FFN[,k,drop=FALSE]
      barploterrbar(EENN,EENN-FFNN,EENN+FFNN,barcol=THECO,legend=LEGENDE,columnForDiffBars=GROUPINGBYSAMPLES,theCut=CUTOFF,ylab=theTitle)
    }}
 if(GROUPINGBYSAMPLES & !is.null(GroupingForPlot)){
   for(k in 1:dim(EEN)[1]){
     EENN <- EEN[k,,drop=FALSE]
     FFNN <- FFN[k,,drop=FALSE]
     barploterrbar(EENN,EENN-FFNN,EENN+FFNN,barcol=GroupingColor,legend=LEGENDE,columnForDiffBars=GROUPINGBYSAMPLES,theCut=CUTOFF,ylab=theTitle,las=2,names.arg=colnames(EENN),main=rownames(EENN),groups=GFPN)
   }}
 dev.off()
}else{
  barploterrbar(EEN,EEN-FFN,EEN+FFN,barcol=THECO,legend=LEGENDE,las=2,columnForDiffBars=GROUPINGBYSAMPLES,theCut=CUTOFF,ylab=theTitle)
  dev.copy(pdf,w=15,h=15,file=savedir(paste(theTitle,"Result",sho,".pdf",sep="")))
  dev.off()}
}
result.bySample <- errBarchart(result)
print(result.bySample)
pdf(getFileName("errbarplot_bySample_", sho), width=15, height=15)
print(result.bySample)
dev.off()
result.byDetector <- errBarchart(result, by="Detector")
print(result.byDetector)
pdf(getFileName("errbarplot_byDetector_", sho), width=15, height=15)
print(result.byDetector)
dev.off()
@
\newpage

\section{Results}

Various files are created during the calculation process. The most important
files are the following:
\begin{itemize}
\item A tab-delimited text file containing all calculated values for each gene/sample combination,
e.g. Ct+error, dCt+error, ddCt+error, level+error. {\bf Name of
the file:} 'allValues' followed by the name of the file containing the
individual Ct values and extension.
\item A .pdf file including the plots, either of the 'levels' or the raw Ct values (depending on your choice)
{\bf Name of the file:} 'Level' or 'Ct' followed by 'Result', the name of the
file containing the individual Ct values and the extension. In this example the name is 'LevelResultTest.pdf'.
\item A tab-delimited text file containing the level and the error of the
level. This file can be used in Excel to create own plots with error
bars. {\bf Name of the file:} 'LevelPlusError' followed by the name of the
file containing the individual Ct values and  extension.
\item A tab-delimited text file containing the results from the t-test and the
Wilcoxon test (if these tests have been performed). {\bf Name of the file:} 'ttest'
followed by the name of the groups used in the test, followed by the name of
the file containing the individual Ct values and extension. In this example we have: 'ttestG1G2Test.txt'.
\item An R object containing all calculated values for each gene/sample combination, e.g. Ct+error,
dCt+error, ddCt+error, level+error. Furthermore the object includes additional sample information.  {\bf Name of
the file:} 'Result' followed by the name of the file containing the individual
Ct values and extension. 

\end{itemize}
There are additional tab-delimited text files and .html files containing
'Ct','dCt', 'ddCt' and 'level' information. All this information is also
included in the  'allValues' file, but in a different format.

% The result of the housekeeping correlation calculation. One plot for each pair
% of housekeeping genes. This plots appear only if you have more than one
% housekeeping gene.

\begin{figure}[htp]
\begin{center}

<<Korrelation,echo=FALSE,results=hide,fig=TRUE,width=15, height=15>>=
A <- name.referenz.gene 
if (length(A) > 1) { 
 if (!is.null(names.of.the.samples.REMAIN.in.cor)){
   corRed <- (rownames(pData(result)) %in% names.of.the.samples.REMAIN.in.cor)
 }else{
   corRed <- !(rownames(pData(result)) %in% names.of.the.samples.NOT.in.cor)
 }
 result2 <- assayData(result[,corRed])$Ct

 K <- combn(1:length(A),2)
 U <- ncol(K)
 par(mfrow=c(ceiling(sqrt(U)),ceiling(sqrt(U))))
 for (i in 1:U){
  Gen1 <- A[K[1,i]] # der Name des ersten Housekeepinggenes
  Gen2 <- A[K[2,i]] # der Name des zweiten Housekeepinggenes
  BART <- cor(result2[Gen1,],result2[Gen2,],use="pairwise.complete.obs")
  if(!is.na(BART)) {
    plot(result2[Gen1,],result2[Gen2,],xlab=Gen1,ylab=Gen2,pch="*",col="red", main=paste("Correlation:",round(BART,3)))
  } else {
    gen1.allna <- all(is.na(result2[Gen1,]))
    warn <- sprintf("Correlation does not exist, since %s in all Samples are 'Undetermined'\n",ifelse(gen1.allna, Gen1, Gen2))

    plot.new()
    text(0.5,0.5, warn)
  }
  }} else{
   if (!is.null(names.of.the.samples.REMAIN.in.cor)){
   corRed <- (rownames(pData(result)) %in% names.of.the.samples.REMAIN.in.cor)
 }else{
   corRed <- !(rownames(pData(result)) %in% names.of.the.samples.NOT.in.cor)
 }
 result2 <- assayData(result[,corRed])$Ct
  plot(result2[A,],pch="*",col="red", main="Expression HK Gene")
 }
@
%
\end{center}
\caption{\label{Korrelation} Correlation of the housekeeping genes}
\end{figure}


<<TTest,echo=FALSE,results=hide>>==

myFoldChange <- function(x){
  x <- as.numeric(x)
  if(length(x) == 2 ){
    resu <- 2^(x[1]-x[2])
  }else if( length(x) == 1  ){
    resu <- 2^x
  }else{
    stop("unexpected situation in myFoldChange")
  }
  return(resu)
}


if(perform.ttest){
ttestName   <- paste("tTests",sho,sep="_")
ttest.path <- getDir(file.path(save.path, ttestName))

if (!is.null(names.of.the.samples.REMAIN.in.ttest)){
  ttestRed <- (rownames(pData(result)) %in% names.of.the.samples.REMAIN.in.ttest)
}else{
  ttestRed <- !(rownames(pData(result)) %in% names.of.the.samples.NOT.in.ttest)
}


result3 <- result[,ttestRed]


daten  <- assayData(result3)$dCt # der t-Test wird immer mit den normalisierten Werten gemacht
if( ! column.for.ttest %in% colnames(pData(result3)) )
  stop(paste(" did not find :", column.for.ttest,": in pData ",sep=""))
faktor <- as.character(pData(result3)[,column.for.ttest])
mmm <- nlevels(as.factor(faktor))
if( mmm == 1 ){
  stop( " found only a single group for t-test ")
}else if( mmm == 2 ){
  aa <- matrix(c(1,2), ncol=1) # aa = die paarweisen Vergleiche
}else{
  aa <- combn(1:mmm,2)
}

 
for (k in 1:ncol(aa)){
  
  Groups  <- levels(as.factor(faktor))[aa[,k]]
  subs    <- faktor %in%  Groups
  datenS  <- daten[,subs]
  faktorS <- as.factor(faktor[subs])  # hier wird gewaerleistet dass der neue Faktor genau zwei Elemente hat
  
  if( ! is.null(column.for.pairs) ){
    if( ! column.for.pairs %in% colnames(pData(result)) )
      stop(paste(" did not find :", column.for.pairs,": in pData ",sep=""))
    paarungS <- as.character(pData(result3)[,column.for.pairs])
    paarungS <- paarungS[subs]
    wenigerRes <- 1
    optTest <- "paired "
  }else{
    wenigerRes <- 0
    optTest <- ""
  }



  res  <- matrix(NA,nrow=nrow(datenS),ncol=8-wenigerRes)
  res2 <- matrix(NA,nrow=nrow(datenS),ncol=2-wenigerRes)

  for (i in 1:nrow(datenS)){ 
    a  <- datenS[i,]
    b  <- is.na(a)
    cc <- a[!b]
    d  <- faktorS[!b]
    if( ! is.null(column.for.pairs) ){ 
      paar <- as.character(paarungS[!b])
      ## restrict to valid pair data only
      validPaarItems <- paar[duplicated(paar)]
      valid <- which( as.character(paar) %in% validPaarItems )
      paar <- paar[valid]
      cc <- cc[valid]
      d <- d[valid]
      if(all(table(d) >1)) {
        sel1 <- which(as.character(d) == Groups[1])
        sel2 <- which(as.character(d) == Groups[2])
        stopifnot( all( paar[sel1][order(paar[sel1])] ==paar[sel2][order(paar[sel2])] )   )
        group1 <- cc[sel1][order(paar[sel1])]
        group2 <- cc[sel2][order(paar[sel2])]
        ff <- t.test(x=group1, y=group2, paired=TRUE)
        ff2<- wilcox.test(x=group1, y=group2, paired=TRUE)
      }else{
        ff <- NULL
        ff2 <- NULL
      }
    }else{
      if(all(table(d)>1)) {
        ff <- t.test(cc~d)
        ff2<- wilcox.test(cc~d)
      }else{
        ff  <- NULL
        ff2 <- NULL
      }
    }
    if( !is.null(ff) ){
      res[i,]  <-c(signif(ff$statistic),
	           signif(ff$p.value),
		   ff2$statistic,
		   signif(ff2$p.value),
		   myFoldChange(ff$estimate),
                   ff$parameter["df"],
                   ff$estimate)          ## 2 Stueck oder 1 Stueck (paired)
      
      res2[i,] <-c(names(ff$estimate))  ## 2 Stueck oder 1 Stueck (paired)
     }
  }
  AllGe      <- rownames(assayData(result)$exprs)
  theHKGenes <- rep("",length(AllGe))
  theHKGenes[AllGe %in% name.referenz.gene] <- "X"
  gg <- cbind(AllGe,theHKGenes,res)
  
   myColnames <- c("Name",
		   "Housekeeping Gene",
                   paste("statistic(", optTest,"t.test)", sep=""),
                   paste("pvalue(",    optTest,"t.test)", sep=""),
                   paste("statistic(", optTest,"Wilcox)", sep=""),
                   paste("pvalue(",    optTest,"Wilcox)", sep=""),
                   "foldChange",
                   "degreeOfFreedom"
                   )
  
  if( ! is.null(column.for.pairs) ){
    Mr1 <- res2[,1]
    extraName <- unique(Mr1[!is.na(Mr1)])
    if( length(extraName) < 1 ){
      extraName <- "fehlenUnklar"
    }
    myColnames <- c(myColnames, extraName) 
  }else{
    Mr1 <- res2[,1]
    Mr2 <- res2[,2]
    stopifnot(length(unique(Mr1[!is.na(Mr1)]))==1 & length(unique(Mr2[!is.na(Mr2)]))==1   )
    myColnames <- c(myColnames,
                    unique(Mr1[!is.na(Mr1)]),
                    unique(Mr2[!is.na(Mr2)])
                    ) 
  }
  
  colnames(gg) <- myColnames
  pVSpalte <- paste("pvalue(",    optTest,"t.test)", sep="")
  gg <-gg[order(as.numeric(gg[,pVSpalte])),]
  
  SAVED <- paste("ttest",Groups[1],Groups[2],sep="")
  write.table(gg,file=file.path(ttest.path, paste(SAVED,"txt",sep=".")),sep="\t",row.names=FALSE)
  write.htmltable(gg,file=file.path(ttest.path,SAVED))
}
}
@

<<BoxplotsHousekkepingGenes,echo=FALSE>>=
if(perform.ttest){
 if (!is.null(names.of.the.samples.REMAIN.in.ttest)){
   ttestRed <- (rownames(pData(result)) %in% names.of.the.samples.REMAIN.in.ttest)
 }else{
   ttestRed <- !(rownames(pData(result)) %in% names.of.the.samples.NOT.in.ttest)
 }
result3 <- result[,ttestRed]
daten   <- assayData(result3)$Ct  # es geht ja hier um die Expressionen der
				 # Housekeeping Gene vor Normalisierung
faktor	<- as.character(pData(result3)[,column.for.ttest])
mmm     <- levels(as.factor((faktor)))
N <- length(mmm)

daten2 <- daten[name.referenz.gene,,drop=FALSE] 
BoxPl <- list()
for (i in 1:N){
 BoxPl[[i]] <- t(daten2[,mmm[i]==faktor])
}

res <- list()
for(i in 1:length(name.referenz.gene)){
 A <- lapply(BoxPl, function(x) x[,i])
 names(A) <- rep(name.referenz.gene[i], N)
 res      <- c(res,A)
}

theColor <- 2 + (1:N)
pdf(file=savedir(paste("HKGenesPerGroup_",sho,".pdf",sep="")),w=15,h=15)
boxplot(res,col=theColor,main="Ct expression of housekeeping genes per group")
dev.off()
}
@

\noindent If warnings have been created during the calculation process they appear here:

<<hh,echo=FALSE,results=hide>>=
if(file.exists(warningFile)){
  bart   <- unlist(read.delim(warningFile,as.is=TRUE,header=FALSE))
  fehler <- sapply(bart, function(y) gsub("simpleWarning in withCallingHandlers\\(\\{: ","",y))
}
@

<<fehler,echo=FALSE>>=
if(file.exists(warningFile))
  fehler
@

\section*{Impressum}
The following packages have been used for the calculation:
<<session, results=tex>>=
toLatex(sessionInfo())
@

\section*{Change Logs}
\subsection*{Version 4.0}
\begin{itemize}
\item Add change logs to the end of the file
\item Add errBarplot for both detector and sample views
\item Adjust to the new ddCt package (developed by Jitao David Zhang
  and Rudolf Biczok) which was submitted to the Bioconductor community
\end{itemize}

\end{document}
