%\VignetteIndexEntry{Additional plots for: Independent filtering increases power for detecting differentially expressed genes, Bourgon et al., PNAS (2010)}
%\VignettePackage{genefilter}
%\VignetteEngine{knitr::knitr}

% To compile this document
% library('knitr'); rm(list=ls()); knit('independent_filtering_plots.Rnw')

\documentclass[10pt]{article}

<<knitr, echo=FALSE, results="hide">>=
library("knitr")
opts_chunk$set(tidy=FALSE,dev="png",fig.show="hide",
               fig.width=4,fig.height=4.5,dpi=240,
               message=FALSE,error=FALSE,warning=FALSE)
@ 

<<style, eval=TRUE, echo=FALSE, results="asis">>=
BiocStyle:::latex()
@

\usepackage{xstring}
\newcommand{\thetitle}{Additional plots for: Independent filtering increases power for detecting differentially expressed genes, Bourgon et al., PNAS (2010)}

\title{\thetitle}
\author{Richard Bourgon}

% The following command makes use of SVN's 'Date' keyword substitution
% To activate this, I used: svn propset svn:keywords Date independent_filtering_plots.Rnw
\date{\Rpackage{genefilter} version \Sexpr{packageDescription("genefilter")$Version} (Last revision \StrMid{$Date$}{8}{18})}

\begin{document}

<<setup, echo=FALSE>>=
options( width = 80 )
@ 

% Make title
\maketitle
\tableofcontents
\vspace{.25in}


%%%%%%%% Main text

\section{Introduction}

This vignette illustrates use of some functions in the
\emph{genefilter} package that provide useful diagnostics 
for independent filtering~\cite{BourgonIndependentFiltering}:

\begin{itemize}
  \item \texttt{kappa\_p} and \texttt{kappa\_t}
  \item \texttt{filtered\_p} and \texttt{filtered\_R}
  \item \texttt{filter\_volcano}
  \item \texttt{rejection\_plot}
\end{itemize}

\section{Data preparation}

Load the ALL data set and the \emph{genefilter} package:

<<libraries>>=
library("genefilter")
library("ALL")
data("ALL")
@

Reduce to just two conditions, then take a small subset of arrays from these,
with 3 arrays per condition:

<<sample_data, cache=TRUE>>=
bcell <- grep("^B", as.character(ALL$BT))
moltyp <- which(as.character(ALL$mol.biol) %in% 
                c("NEG", "BCR/ABL"))
ALL_bcrneg <- ALL[, intersect(bcell, moltyp)]
ALL_bcrneg$mol.biol <- factor(ALL_bcrneg$mol.biol)
n1 <- n2 <- 3
set.seed(1969)
use <- unlist(tapply(1:ncol(ALL_bcrneg), 
                     ALL_bcrneg$mol.biol, sample, n1))
subsample <- ALL_bcrneg[,use]
@

We now use functions from \emph{genefilter} to compute overall standard devation
filter statistics as well as standard two-sample $t$ and releated statistics.

<<stats, cache=TRUE>>=
S <- rowSds( exprs( subsample ) )
temp <- rowttests( subsample, subsample$mol.biol )
d <- temp$dm
p <- temp$p.value
t <- temp$statistic
@ 


\section{Filtering volcano plot}

Filtering on overall standard deviation and then using a standard $t$-statistic
induces a lower bound of fold change, albeit one which varies somewhat with the
significance of the $t$-statistic. The \texttt{filter\_volcano} function allows
you to visualize this effect.

<<filter_volcano, include=FALSE>>=
S_cutoff <- quantile(S, .50)
filter_volcano(d, p, S, n1, n2, alpha=.01, S_cutoff)
@ 
The output is shown in the left panel of Fig.~\ref{fig:volcano}.

\begin{figure}[tb]
\begin{center}
\includegraphics[width=0.49\textwidth]{figure/filter_volcano-1}
\includegraphics[width=0.49\textwidth]{figure/kappa-1}
\caption{Left panel: plot produced by the \texttt{filter\_volcano} function.
Right panel: graph of the \texttt{kappa\_t} function.}
\label{fig:volcano}
\end{center}
\end{figure}

The \texttt{kappa\_p} and \texttt{kappa\_t} functions, used to make the volcano
plot, compute the fold change bound multiplier as a function of either a
$t$-test $p$-value or the $t$-statistic itself. The actual induced bound on the
fold change is $\kappa$ times the filter's cutoff on the overall standard
deviation. Note that fold change bounds for values of $|T|$ which are close to 0
are not of practical interest because we will not reject the null hypothesis
with test statistics in this range.

<<kappa, include=FALSE>>=
t <- seq(0, 5, length=100)
plot(t, kappa_t(t, n1, n2) * S_cutoff, 
     xlab="|T|", ylab="Fold change bound", type="l")
@
The plot is shown in the right panel of Fig.~\ref{fig:volcano}.



\section{Rejection count plots}

\subsection{Across $p$-value cutoffs}

The \texttt{filtered\_p} function permits easy simultaneous calculation of
unadjusted or adjusted $p$-values over a range of filtering thresholds
($\theta$). Here, we return to the full ``BCR/ABL'' versus ``NEG'' data set, and
compute adjusted $p$-values using the method of Benjamini and Hochberg, for a
range of different filter stringencies.

\begin{figure}[tb]
\begin{center}
\includegraphics[width=0.49\textwidth]{figure/rejection_plot-1}
\includegraphics[width=0.49\textwidth]{figure/filtered_R_plot-1}
\caption{Left panel: plot produced by the \texttt{rejection\_plot} function.
Right panel: graph of \texttt{theta}.}
\label{fig:rej}
\end{center}
\end{figure}


<<table>>=
table(ALL_bcrneg$mol.biol)
@

<<filtered_p>>=
S2 <- rowVars(exprs(ALL_bcrneg))
p2 <- rowttests(ALL_bcrneg, "mol.biol")$p.value
theta <- seq(0, .5, .1)
p_bh <- filtered_p(S2, p2, theta, method="BH")
@

<<p_bh>>=
head(p_bh)
@ 

The \texttt{rejection\_plot} function takes sets of $p$-values corresponding to
different filtering choices --- in the columns of a matrix or in a list --- and
shows how rejection count ($R$) relates to the choice of cutoff for the
$p$-values. For these data, over a reasonable range of FDR cutoffs, increased
filtering corresponds to increased rejections.

<<rejection_plot>>=
rejection_plot(p_bh, at="sample",
               xlim=c(0,.3), ylim=c(0,1000),
               main="Benjamini & Hochberg adjustment")
@
The plot is shown in the left panel of Fig.~\ref{fig:rej}.




\subsection{Across filtering fractions}

If we select a fixed cutoff for the adjusted $p$-values, we can also look more
closely at the relationship between the fraction of null hypotheses filtered and
the total number of discoveries. The \texttt{filtered\_R} function wraps
\texttt{filtered\_p} and just returns rejection counts. It requires a $p$-value
cutoff. 

<<filtered_R>>=
theta <- seq(0, .80, .01)
R_BH <- filtered_R(alpha=.10, S2, p2, theta, method="BH")
@

<<R_BH>>=
head(R_BH)
@

Because overfiltering (or use of a filter which is inappropriate for the
application domain) discards both false and true null hypotheses, very large
values of $\theta$ reduce power in this example:

<<filtered_R_plot>>=
plot(theta, R_BH, type="l",
     xlab=expression(theta), ylab="Rejections",
     main="BH cutoff = .10"
     )
@
The plot is shown in the right panel of Fig.~\ref{fig:rej}.



%%%%%%%% Session info

\section*{Session information}

<<sessionInfo, results='asis', echo=FALSE>>=
si <- as.character( toLatex( sessionInfo() ) )
cat( si[ -grep( "Locale", si ) ], sep = "\n" )
@


\begin{thebibliography}{10}
\bibitem{BourgonIndependentFiltering}
Richard Bourgon, Robert Gentleman and Wolfgang Huber.
\newblock Independent filtering increases power for detecting differentially
expressed genes.
\end{thebibliography}



\end{document}
