<%@meta language="R-vignette" content="--------------------------------
  DIRECTIVES FOR R:

  %\VignetteIndexEntry{The babel vignette}
  %\VignetteKeyword{example, R, package, vignette}
  %\VignetteAuthor{Adam B. Olshen, Richard A. Olshen, and Barry S. Taylor}
  %\VignetteEngine{R.rsp::rsp}
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\documentclass[10pt]{article}
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\usepackage{graphicx}
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R.utils::use("R.utils, R.devices")
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\graphicspath{{<%=getOption("devEval/args/path", "figures/")%>}}

\title{<%@meta name="title"%>}
\author{<%@meta name="author"%>}

\begin{document}
\maketitle

\section{Introduction}

This document presents an overview of the {\tt babel} package. This
package is for analyzing ribosome profiling data.  Ribosome profiling
data arises from paired next generation sequencing experiments
~\cite{aingolia09,bhsieh12}.  One experiment is standard RNA-seq, which
we abbreviate as mRNA.  The other is RNA-seq of ribosome-protected
fragments, which we abbreviate as RPF.  It implements our methodology
for finding genes that have unusual RPF counts given their mRNA counts
~\cite{colshen13}.  

Our model is that the mRNA counts follow a negative binomial
distribtion, as is standard ~\cite{drobinson08,eanders10}.  Because
sample sizes are always low in this context, we estimate a single
over-dispersion parameter across genes using the Robinson and Smyth
methodology found in edgeR ~\cite{frobinson10}.  Given the mRNA
counts, we model the RPF counts also as negative binomial with mean
according to a trimmed least sequares regression fit of RPF counts on
mRNA counts and over-dispersion estimated in an iterative fashion
~\cite{colshen13}.

As we detail below, our package does three main things.  First, we
identify genes with unusual RPF counts given their mRNA counts {\it
  within} conditions.  Second, we combine p-values across multiple
experiments within conditions.  Third, we use the within-condition
p-values to identify genes where the RPF/mRNA relationship has changed
{\it between} conditions.

\section{Data}

We selected a ribosome profiling data set consisting of 1000 genes
from two conditions (GroupA and GroupB) with each condition having two
replicates (Sample1 and Sample2).  We are calling this data set {\tt
ribo.prof}.

\section{An Example}

Here we perform an analysis on the {\tt ribo.prof} data described above.  First we load the library and data.

\begin{verbatim}<%=withCapture({
library(babel)
data(ribo.prof)
})%>\end{verbatim}

Next we print the first five lines of the mRNA ({\tt test.rna}) and
RPF ({\tt test.rp}) elements of {\tt ribo.prof}.  Note that these are
raw counts that are not normalized in any way.  Raw counts must be
used with {\tt babel}.

\begin{verbatim}<%=withCapture({
test.rna <- ribo.prof$test.rna
print(test.rna[1:5,])
})%>\end{verbatim}
   
\begin{verbatim}<%=withCapture({
test.rp <- ribo.prof$test.rp
print(test.rp[1:5,])
})%>\end{verbatim}

We see paired count data for the first five genes.  We plot
the Sample1 and GroupA data, with both mRNA and RPF counts on the log
scale.  We expect and actually do see an increasing function between
the mRNA counts and RPF counts.
   
\begin{figure}[htbp]
 \begin{center}
  \resizebox{0.6\textwidth}{!}{
   \includegraphics{<%=toPDF("scatter", tags="1A", {
   plot(test.rna[,1]+1,test.rp[,1]+1,xlab="mRNA counts",ylab="RPF counts",pch=16,log="xy",xlim=c(1,10000),ylim=c(1,10000),font.lab=2)
   })%>}
  }
  \caption{Scatterplot of Sample1 and GroupA data.}
  \label{fig:scatter}
 \end{center}
\end{figure}

Similar patterns are repeated across the three other
experiments.  Next we estimate p-values for the RPF counts given the
mRNA counts within every experiment.  The tests are one-sided; higher
than expected RPF counts lead to low p-values, while lower than
expected RPF counts lead to high p-values.

We set group labels corresponding to the columns of the mRNA
and RPF inputs.
\begin{verbatim}<%=withCapture({
test.group <- c("A","B","A","B")
})%>\end{verbatim}

Then, for the purpose of reproducibility, we fix the number
of cores at 1.  Two cores are used by default, unless the machine is
running Windows, where one is the default because Windows cannot use
the fork command in the parallel library.  Users are encouraged to use
multiple cores if they are available.  Future versions of the software
will allow computation over multiple nodes of a computational cluster.
\begin{verbatim}<%=withCapture({
options(mc.cores=1)
})%>\end{verbatim}

We set the seed for reproducibility purposes.
\begin{verbatim}<%=withCapture({
set.seed(12345)
})%>\end{verbatim}

We run the main function, which is called {\tt babel()}.  The argument
{\tt nreps} is set to 100000.  This is the number of permutations, so
the minimum p-value is {\tt 1/nreps}.  Therefore, when correcting for
multiple comparisons, We would prefer a million, or, even better, ten
million reps, but here we are just demonstrating the procedure on one
core.  The argument {\tt min.rna} is set to $10$.  This is the minimum
number of mRNA counts acrossing all experiments for a gene to be
included.  This cutoff leads to the removal of $9$ genes so that $991$
are analyzed.
\begin{verbatim}<%=withCapture({
test.babel <- babel(test.rna,test.rp,group=test.group,nreps=1e+05,min.rna=10)
})%>\end{verbatim}

Now we examine the first five lines of the within element of the list
created by the {\tt babel()} run.
 
\begin{verbatim}<%=withCapture({
within.babel <- test.babel$within
print(within.babel[[1]][1:5,])
})%>\end{verbatim}

The element {\tt Direction} is whether the RPF count is
greater (1) or less (-1) than expected given the mRNA count.  The {\tt
P-value (one-sided)} tells us how unusual the RPF count is given the
mRNA count, with low or high being interesting.  The {\tt P-value
(two-sided)} correspond to how unusual the RPF count is relative to
the mRNA count, with only low being interesting.  The {\tt FDR} is the
estimated false discovery rate corresponding to the gene for the
two-sided p-value.

We plot the interesting genes from the within analysis on this sample.
Genes in red have (one-sided) p-values $< 0.025$, while genes in green
have p-values $> 0.975.$

\begin{figure}[htbp]
 \begin{center}
  \resizebox{0.6\textwidth}{!}{
   \includegraphics{<%=toPDF("scatter2", tags="1A", {
   which.025 <- which(within.babel[[1]]$"P-value (one-sided)"<0.025)
   which.975 <- which(within.babel[[1]]$"P-value (one-sided)">0.975)	
   plot(test.rna[,1]+1,test.rp[,1]+1,xlab="mRNA counts",ylab="RPF counts",pch=16,log="xy",xlim=c(1,10000),ylim=c(1,10000),font.lab=2)
   points(test.rna[which.025,1]+1,test.rp[which.025,1]+1,pch=16,col=2)
   points(test.rna[which.975,1]+1,test.rp[which.975,1]+1,pch=16,col=3)
   })%>}
  }
  \caption{Scatterplot of Sample1 and GroupA data with unusual genes highlighted.}
  \label{fig:scatter2}
 \end{center}
\end{figure}

Next we combine the p-values from the two experiments from
Group A using the technique described in our manuscript ~\cite{colshen13},
and show the first five lines from the results.  Note there is now a
single (two-sided) combined p-value and a corresponding FDR.  The
Direction let us know whether RPF counts are higher or lower than
expected.  

\begin{verbatim}<%=withCapture({
combined.babel <- test.babel$combined
print(combined.babel[[1]][1:5,])
})%>\end{verbatim}

We plot the genes at the same p-value cutoffs as before.
Note the increase in power from combining experiments and that all
highlighted genes are unusual in both samples.

\begin{figure}[htbp]
 \begin{center}
  \resizebox{0.6\textwidth}{!}{
   \includegraphics{<%=toPDF("scatter3", tags="1A", {
   which.025 <- which(combined.babel[[1]]$"P-value"<0.025)
   plot(test.rna[,1]+1,test.rp[,1]+1,xlab="mRNA counts",ylab="RPF counts",pch=16,log="xy",xlim=c(1,10000),ylim=c(1,10000),font.lab=2)
   points(test.rna[which.025,1]+1,test.rp[which.025,1]+1,pch=16,col=2)
   })%>}
  }
  \caption{Scatterplot of the Sample1 and GroupA data with unusual genes
 highlighted from combined analysis.}
  \label{fig:scatter3}
 \end{center}
\end{figure}

\begin{figure}[htbp]
 \begin{center}
  \resizebox{0.6\textwidth}{!}{
   \includegraphics{<%=toPDF("scatter4", tags="1A", {
   which.025 <- which(combined.babel[[1]]$"P-value"<0.025)
   plot(test.rna[,3]+1,test.rp[,3]+1,xlab="mRNA counts",ylab="RPF counts",pch=16,log="xy",xlim=c(1,10000),ylim=c(1,10000),font.lab=2)
   points(test.rna[which.025,3]+1,test.rp[which.025,3]+1,pch=16,col=2)
   })%>}
  }
  \caption{Scatterplot of the Sample2 and GroupA data with unusual genes
 highlighted from combined analysis.}
  \label{fig:scatter4}
 \end{center}
\end{figure}

Next we look for genes whose RPF to mRNA counts vary between
group A and group B.  Here there are only two groups, but babel
automatically tests all pairwise combinations.  We again print the
first five lines of the output.

\begin{verbatim}<%=withCapture({
between.babel <- test.babel$between
print(between.babel[[1]][1:5,])
})%>\end{verbatim}

The elements {\tt mRNA$\_$logFC} and {\tt mRNA$\_$FDR} are based on
tests for differential expression just on the mRNA data
~\cite{frobinson10}.  Genes with {\tt mRNA$\_$FDR} $< 0.05$ are labeled as
"both," meaning change is in expression and translation; other genes
are labeled "translation$\_$only".  There is a single (two-sided)
p-value for the difference in RPF relative to mRNA and the
corresponding FDR.  The element {\tt Direction} determines the type of
change (1 for translation higher in the first group label, -1 for
lower in the first group label).  Note that an FDR of $25\%$ there are
$11$ significant genes.

<%-------------------------------------------------------------------
  REFERENCES
  -------------------------------------------------------------------%>
\bibliographystyle{ieeetr}
\bibliography{babel}

<%-------------------------------------------------------------------
  APPENDIX
  -------------------------------------------------------------------%>
\clearpage
\section*{Appendix}
\subsection*{Session information}
<%=toLatex(sessionInfo())%>
This report was generated using the R.rsp package.

\end{document}

