Sec-objects.Rnw

<<env0, echo=FALSE, message = FALSE>>=
library("Biobase")
data(sample.ExpressionSet)
@ 

<<knitr, echo=FALSE>>=
opts_chunk$set(tidy.opts = 
               list(width.cutoff = 50, 
                    tidy = FALSE),
               fig.align = 'center',
               stop_on_error = 1L,
               comment = NA,
               prompt = TRUE)
options(width = 60)
@ 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%                 subsection                 %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\subsection{The \Robject{eSet} class}

\begin{frame}{The \Robject{eSet} class}
  \begin{block}{Higher order objects}
  When the data to be stored is more complex, special objects are created to store and handle it in a specialised manner. 
  These higher order objects are constructed using the data types we have seen so far as building blocks.
  
  \bigskip
  Let's look at how microarray data is handled in Bioconductor - the \Robject{eSet} structure.
  
  \bigskip
  (The \Robject{eSet} model has been re-used for other technologies.)
  \end{block}
\end{frame}

\begin{frame}[fragile]
<<eset>>=
library("Biobase")
data(sample.ExpressionSet)
sample.ExpressionSet
@ 
\end{frame}

\begin{frame}[fragile]
<<eset2>>=
class(sample.ExpressionSet)
slotNames(sample.ExpressionSet)
@ 
<<eset2b, eval=FALSE>>=
## class?ExpressionSet
@ 
\end{frame}

\begin{frame}
  \begin{description}
  \item[assayData] expression values in identical sized matrices.
  \item[phenoData] sample annotation in \Robject{AnnotatedDataFrame}.
  \item[featureData] feature annotation in \Robject{AnnotatedDataFrame}.
  \item[experimentData] description of the experiment as a \Robject{MIAME} object (see \Rfunction{?MIAME} for more details).
  \item[annotation] type of chip as a \Robject{character}.
  \item[protocolData] scan dates as a \Robject{character}.   
  \end{description}
\end{frame}


\begin{frame}[fragile]
  \begin{block}{The \Robject{assayData} slot}
    Stored the expression data of the assay.
<<eset3>>=
exprs(sample.ExpressionSet)[1:4, 1:3]
dim(sample.ExpressionSet)
@   
  \end{block}
\end{frame}

\begin{frame}[fragile]
  \begin{block}{The \Robject{phenoData} slot}
    stores the meta data about the samples.
<<eset4>>=
phenoData(sample.ExpressionSet)
@   
<<eset4b, eval=FALSE>>=
pData(sample.ExpressionSet) ## as a data.frame
@
  \end{block}
\end{frame}

\begin{frame}[fragile]
  \begin{block}{The \Robject{featureData} slot}
    stores the meta data about the feautres.
<<eset5>>=
fData(sample.ExpressionSet)
@   
<<eset5b, eval=FALSE>>=
## as an AnnotatedDataFrame
featureData(sample.ExpressionSet) 
@
  \end{block}
\end{frame}

\begin{frame}[fragile]
  \begin{block}{\Robject{AnnotatedDataFrame}}
    consists of a collection of samples and the values of variables measured on those samples. 
    There is also a description of each variable measured.    
    \Robject{AnnotatedDataFrame} associates a \Robject{data.frame} with its metadata.
<<pdata>>=
head(pData(sample.ExpressionSet))
@ 
  \end{block}  
\end{frame}

\begin{frame}[fragile]
  \begin{block}{Subsetting \Robject{ExpressionSet} instances}
    It is reasonable to expect that subsetting operations work also for higher order objects. 
<<subexprs>>=
sample.ExpressionSet[1:10, 1:2]
@     
  \end{block}
\end{frame}