%\VignetteIndexEntry{How to find genes whose expression profile is similar to that of specified genes}
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\begin{document}
\title{How to find genes whose expression profile is similar to that of specified genes}

\maketitle

\section*{Introduction}

In some cases you have certain genes of interest and you would like to
find other genes that are {\em close} to the genes of interest.
This can be done using the \verb+genefinder+ function.

You need to specify either the index position of the genes you want
(which row of the expression array the gene is in) or the name
(consistent with the \verb+featureNames+ of the ExpressionSet).

A vector of names can be specified and matches for all will be
computed. The number of matches and the distance measure used can all
be specified.
The examples will be carried out using the artificial data set,
\verb+sample.ExpressionSet+.

Two other options for \verb+genefinder+ are \verb+scale+ and \verb+method+.
The \verb+scale+ option controls the scaling of the rows (this is
often desirable) while the \verb+method+ option controls the distance
measure used between genes. The possible values and their meanings are
listed at the end of this document.

<<>>=
 library("Biobase")
 library("genefilter")
 data(sample.ExpressionSet)
 igenes<- c(300,333,355,419) ##the interesting genes
 closeg <- genefinder(sample.ExpressionSet, igenes, 10, 
       method="euc", scale="none")
 names(closeg)
@

The Affymetrix identifiers (since these were originally Affymetrix
data) are \verb+31539_r_at+, \verb+31572_at+, \verb+31594_at+ and
\verb+31658_at+.
We can find the nearest genes (by index) for any of these by simply
accessing the relevant component of \verb+closeg+.

<<>>=
closeg$"31539_r_at"
Nms1 <- featureNames(sample.ExpressionSet)[closeg$"31539_r_at"$indices]
Nms1
@
%$
You could then take these names (from \verb+Nms1+) and the {\em
  annotate} package and explore them further. See the various HOWTO's
  in annotate to see how to further explore your data.
Examples include finding and searching all PubMed abstracts associated
  with these data. Finding and downloading associated sequence information.
The data can also be visualized using the {\em geneplotter} package
  (again there are a number of HOWTO documents there).


\section*{Parameter Settings}

The \verb+scale+ parameter can take the following values:
\begin{description}
\item[none] No scaling is done.
\item[range] Scaling is done by $(x_i - x_{(1)})/(x_{(n)}- x_{(1)})$.
\item[zscore] Scaling is done by $(x_i - \bar{x})/ s_x$. Where $s_x$
  is the standard deviation.
\end{description}

The \verb+method+ parameter can take the following values:
\begin{description}
\item[euclidean] Euclidean distance is used.
\item[maximum]  Maximum distance between any two elements of x and y
          (supremum norm).
\item[manhattan] Absolute distance between the two vectors (1 norm).

 \item[canberra] The $\sum (|x_i - y_i| / |x_i + y_i|)$.  Terms with zero
          numerator and denominator are omitted from the sum and
          treated as if the values were missing.
\item[binary] (aka asymmetric binary): The vectors are regarded as
          binary bits, so non-zero elements are {\em on} and zero elements
          are {\em off}.  The distance is the proportion of bits in which
          only one is on amongst those in which at least one is on.
\end{description}

\section*{Session Information}

The version number of R and packages loaded for generating the vignette were:

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toLatex(sessionInfo())
@

\end{document}