\HeaderA{gapFilter}{A filter to select genes based on there being a gap.}{gapFilter}
\keyword{manip}{gapFilter}
\begin{Description}\relax
The \code{gapFilter} looks for genes that might usefully discriminate
between two groups (possibly unknown at the time of filtering).
To do this we look for a gap in the ordered expression values. The gap
must come in the central portion (we exclude jumps in the initial
\code{Prop} values or the final \code{Prop} values).
Alternatively, if the IQR for the gene is large that will also pass
our test and the gene will be selected.
\end{Description}
\begin{Usage}
\begin{verbatim}
gapFilter(Gap, IQR, Prop, na.rm=TRUE, neg.rm=TRUE)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{Gap}] The size of the gap required to pass the test. 
\item[\code{IQR}] The size of the IQR required to pass the test. 
\item[\code{Prop}] The proportion (or number) of samples to exclude at either
end.
\item[\code{na.rm}] If \code{TRUE} then \code{NA}'s will be removed before
processing. 
\item[\code{neg.rm}] If \code{TRUE} then negative values in \code{x} will be
removed before processing.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
As stated above we are interested in
\end{Details}
\begin{Value}
A function that returns either \code{TRUE} or \code{FALSE} depending on
whether the vector supplied has a gap larger than \code{Gap} or an IQR
(inter quartile range) larger than \code{IQR}. For computing the gap we
want to exclude a proportion, \code{Prop} from either end of the sorted
values. The reason for this requirement is that genes which differ in
expression levels only for a few samples are not likely to be interesting.
\end{Value}
\begin{Author}\relax
R. Gentleman
\end{Author}
\begin{SeeAlso}\relax
\code{\LinkA{ttest}{ttest}}, \code{\LinkA{genefilter}{genefilter}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
 set.seed(256)
 x <- c(rnorm(10,100,3), rnorm(10, 100, 10))
 y <- x + c(rep(0,10), rep(100,10))
 tmp <- rbind(x,y) 
 Gfilter <- gapFilter(200, 100, 5)
 ffun <- filterfun(Gfilter)
 genefilter(tmp, ffun)
\end{ExampleCode}
\end{Examples}