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\title{The bumphunter user's guide}
\author{Kasper Daniel Hansen \texttt{khansen@jhsph.edu}
  \and
  Martin Aryee \texttt{aryee.martin@mgh.harvard.edu}
  \and
  Rafael A. Irizarry \texttt{rafa@jhu.edu}
}
\date{Modified: November 23, 2012.  Compiled: \today}
\begin{document}
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\maketitle

\section*{Introduction}

This package implements the statistical procedure described in \cite{Jaffe:2012} (with some small
modifications).  Notably, batch effect removal needs additional code.

For any given type of data, it is usually necessary to make a number of choices and/or
transformations before the bump hunting methodology is ready to be applied.  Typically, these
modifications resides in other packages.  Examples are \Rpackage{charm} (for CHARM-like methylation
microarrays), \Rpackage{bsseq} (for whole-genome bisulfite sequencing data) and \Rpackage{minfi}
(for Illumina 450k methylation arrays).  In some cases (specifically \Rpackage{bsseq}) only parts of
the methodology as implemented in the \Rpackage{bumphunter} package is applied, although the
conceptual approach is still build on bump hunting.

In other words, this package is mostly intended for developers wishing to adapt the general
methodology to their specific applications.

The core of the package is encapsulated in the \Rcode{bumphunter} method which uses the underlying
\Rcode{bumphunterEngine} to do the heavy lifting.  However, \Rcode{bumphunterEngine} consists of a
number of useful functions that does much of the specific tasks involved in bump hunting.  This
document attempts to describe the overall workflow as well as the specific functions.  The relevant
functions are \Rcode{clusterMaker}, \Rcode{getSegments}, \Rcode{findRegions}.


<<libload>>=
library(bumphunter)
@

Note that this package is written with genomic data as an illustrative example but most of it is
easily generalizable to other data types.

\subsection*{Other functions}

Most of the \Rpackage{bumphunter} package is code for bump hunting.  But we also include a number of
convenience functions we have found useful, which are not necessarily part of the bump hunting
exercise.  At the time of writing, this include \Rcode{annotateNearest}.

\section*{The Statistical Model}

The bump hunter methodology is meant to work on data with several biological replicates, similar to
the \Rcode{lmFit} function in \Rpackage{limma}.  While the package is written using genomic data as
an illustrative example, most of it is generalizable to other data types (with some one-dimensional
location information).

We assume we have data $Y_{ij}$ where $i$ represents (biological) replicate and $l_j$ represents
genomic location.  The use of $j$ and $l_j$ is a convenience notation, allowing us to discuss the
``next'' observation $j+1$ which may be some distance $l{j+1} - l_j$ away.  Note that we assume in
this notation that all replicates have been observed at the same set of genomic locations.

The basic statistical model is the following:
\begin{displaymath}
Y_{ij} = \beta_0(l_j) + \beta_1(l_j) X_j + \varepsilon_{ij}
\end{displaymath}
with $i$ representing subject, $l_j$ representing the $j$th location, $X_j$ is the covariate of interest (for
example $X_j=1$ for cases and $X_j=0$ otherwise), $\varepsilon_{ij}$ is measurement error, $\beta_0(l)$ is
a baseline function, and $\beta_1(l)$ is the parameter of interest, which is a function of
location.  We assume that $\beta_1(l)$ will be equal to zero over most of the genome, and we want to
identify stretched where $\beta_1(l) \neq 0$, which we call \emph{bumps}.

We want to share information between nearby locations, typically through some form of smoothing.

\section*{Creating clusters}

For many genomic applications the locations are clustered.  Each cluster is a distinct unit where
the model fitting will be done separately, and each cluster does not depend on the data, only on the
locations $l_j$.  Typically there is some maximal distance, and we do not want to smooth between
observations separated by more than this distance.  The choice of distance is very application dependent.

``Clusters'' are simply groups of locations such that two consecutive locations in the cluster are
separated by less than some distance \Rcode{maxGap}.  For genomic applications, the biggest possible
clusters are chromosomes.

The function \Rcode{clusterMaker} defines such grouping locations.

Example: We first generate an example of typical genomic locations
<<clustermakerdata>>=
pos <- list(pos1=seq(1,1000,35),
            pos2=seq(2001,3000,35),
            pos3=seq(1,1000,50))
chr <- rep(paste0("chr",c(1,1,2)), times = sapply(pos,length))
pos <- unlist(pos, use.names=FALSE)
@

Now we run the function to obtain the three clusters from the positions. We use the default gap of
300 base pairs (bps), i.e. any two points more than 300 bps away are put in a new cluster. Also,
locations from different chromosomes are separated.

<<clustermaker>>=
cl <- clusterMaker(chr, pos, maxGap = 300)
table(cl)
@

The output is an indexing variable telling us which cluster each location belongs to.  Locations on
different chromosomes are always on different clusters.


Note that data from the first chromosome has been split into two clusters:
<<clusterplot,fig=TRUE,width=6,height=3>>=
ind <- which(chr=="chr1")
plot(pos[ind], rep(1,length(ind)), col=cl[ind],
     xlab="locations", ylab="")
@

\section*{Breaking into segments}

The function \Rcode{getSegments} is used to find segments that are positive, near zero, and
negative.  Specifically we have a vector of numbers $\theta_j$ with each number associated with a
genomic location $l_j$ (thinks either test statistics or estimates of $\beta_i(l)$).  A segment is a
list of consecutive locations such that all $\theta_l$ in the segment are either ``positive'', ``near zero'' or
``negative''.  In order to define ``positive'' etc we need a \Rcode{cutoff} which is one number $L$ (in
which case ``near zero'' is $[-L,L]$) or two numbers $L,U$ (in which case ``near zero'' is $[L;
U]$).


Example: we are going to create a simulated $\beta_1(l)$ with a couple of real bumps.
<<simulatedbumps>>=
Indexes <- split(seq_along(cl), cl)
beta1 <- rep(0, length(pos))
for(i in seq(along=Indexes)){
    ind <- Indexes[[i]]
    x <- pos[ind]
    z <- scale(x, median(x), max(x)/12)
    beta1[ind] <- i*(-1)^(i+1)*pmax(1-abs(z)^3,0)^3 ##multiply by i to vary size
}
@

We now find bumps of this functions by

<<getSegments>>=
segs <- getSegments(beta1, cl, cutoff=0.05)
@

Now we can make, for example, a plot of all the positive bumps

<<plotSegments,fig=TRUE,width=7,height=4>>=
par(mfrow=c(1,2))
for(ind in segs$upIndex){
    index <- which(cl==cl[ind[1]])
    plot(pos[index], beta1[index],
         xlab=paste("position on", chr[ind[1]]),
         ylab="beta1")
    points(pos[ind], beta1[ind], pch=16, col=2)
    abline(h = 0.05, col = "blue")
}
@

This function is used by regionFinder which is described next.

\section*{regionFinder}

This function packages up the results of \Rcode{getSegments} into a table of regions with the
location and characteristics of bumps.
<<regionFinder>>=
tab <- regionFinder(beta1, chr, pos, cl, cutoff=0.05)
tab
@

In the plot in the preceding section we show two of these regions in red.

Note that \Rcode{regionFinder} and \Rcode{getSegments} do not really contain any statistical model.
All it does is finding regions based on segmenting a vector $\theta_j$ associated with genomic
locations $l_j$.

\section*{Bumphunting}

\Rcode{Bumphunter} is a more complicated function.  In addition to \Rcode{regionFinder} and
\Rcode{clusterMaker} it also implements a statistical model as well as permutation testing to assess
uncertainty.

We start by creating a simulated data set of 10 cases and 10 controls (recall that \Rcode{beta1} was
defined above).

<<simulationOfReps>>=
beta0 <- 3*sin(2*pi*pos/720)
X <- cbind(rep(1,20), rep(c(0,1), each=10))
error <- matrix(rnorm(20*length(beta1), 0, 1), ncol=20)
y <- t(X[,1])%x%beta0 + t(X[,2])%x%beta1 + error
@

Now we can run \Rcode{bumphunter}
<<bumphunter>>=
tab <- bumphunter(y, X, chr, pos, cl, cutoff=.5)
tab
names(tab)
tab$table
@

Briefly, the \Rcode{bumphunter} function fits a linear model for each location (like \Rcode{lmFit} from the
\Rpackage{limma} package), focusing on one specific column (coefficient) of the design matrix.  This
coefficient of interest is optionally smoothed.  Subsequently, a permutation test is formed for this
specific coefficient.

\section*{Faster bumphunting with multiple cores}

\Rcode{bumphunter} can be speeded up by using multiple cores.  We use the \Rcode{foreach} package
which allows different parallel "back-ends" that will distribute the computation across multiple
cores in a single machine, or across multiple machines in a cluster.  The most straight-forward
usage, illustrated below, involves multiple cores on a single machine.  See the \Rcode{foreach}
documentation for more complex use cases, as well as the packages \Rcode{doParallel} and
\Rcode{doSNOW} (among others).  Finally, we use \Rcode{doRNG} to ensure reproducibility of setting
the seed within the parallel computations.

In order to use the \Rcode{foreach} package we need to register a backend, in this case a multicore
machine with 2 cores.

<<load-foreach>>=
library(doParallel)
registerDoParallel(cores = 2)
@

\Rcode{bumphunter} will now automatically use this backend

<<parallel-bumphunter>>=
tab <- bumphunter(y, X, chr, pos, cl, cutoff=.5, B=250, verbose = TRUE)
tab
@

\bibliographystyle{plain}
\bibliography{bumphunter}

\section*{Cleanup}

This is a cleanup step for the vignette on Windows; typically not needed for users.

<<closeConnetions,results=hide>>=
bumphunter:::foreachCleanup()
@

\section*{SessionInfo}

<<sessionInfo,results=tex,eval=TRUE,echo=FALSE>>=
toLatex(sessionInfo())
@

\end{document}

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