\HeaderA{proboscis}{Produce a plot similar to Figure 2 of the Shippy MAQC paper (PMID 16964226).}{proboscis}
\keyword{models}{proboscis}
\begin{Description}\relax
Produce a plot similar to Figure 2 of the Shippy MAQC paper (PMID 16964226).
\end{Description}
\begin{Usage}
\begin{verbatim}
proboscis(es, site=1, ABp=0.001, CDp=0.01, mmrad=100)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{es}] \code{\LinkA{ExpressionSet}{ExpressionSet}} instance with MAQC assay results
\item[\code{site}] numeric code -- site to be assessed
\item[\code{ABp}] ABp -- p-value threshold to declare concentration of gene in
sample A to be different from ehe concentration in sample B
\item[\code{CDp}] CDp -- p-value threshold to declare concentration of gene in
sample C to be different from the concentration in sample D
\item[\code{mmrad}] numeric radius of the moving mean used to smooth the proportions
differentially expressed 
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
Figure 2 of the Shippy paper consists of a collection of plots of
estimated probabilities of self-consistent monotone titration -- briefly,
samples are such that A has 100\% USRNA, B has 100\% Ambion brain,
C has 75\% USRNA+25\% brain, D has 25\% USRNA, 75\% brain.  Self-consistent
monotone titration holds for gene g if microarray measures for that gene
satisfy A > C > D > B or B > C > D > A.  The estimated probability functions
look like a creature sticking its nose over a wall, thus the name of this function.
\end{Details}
\begin{Value}
an instance of \code{\LinkA{proboStruct}{proboStruct.Rdash.class}}, for which a plot and lines method are available.
\end{Value}
\begin{Author}\relax
Vince Carey <stvjc@channing.harvard.edu>
\end{Author}
\begin{References}\relax
PMID 16964226
\end{References}
\begin{Examples}
\begin{ExampleCode}
data(afxsubRMAES)
NN2 = proboscis(afxsubRMAES, site=2)
plot(NN2)
\end{ExampleCode}
\end{Examples}

