%\VignetteIndexEntry{GenomicRanges Use Cases}
%\VignetteDepends{EatonEtAlChIPseq, leeBamViews, Rsamtools, GenomicFeatures,
%  org.Sc.sgd.db, BSgenome.Scerevisiae.UCSC.sacCer2}
%\VignetteKeywords{sequence, sequencing, alignments}
%\VignettePackage{GenomicRanges}
\documentclass[10pt]{article}

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% TODO: Add example showing (for RNASeq) how to convert the results into a table such as can be used by DESeq and include in that example a conversion of the gene IDs to the gene symbols. 
\title{GenomicRanges Use Cases}
\author{Marc Carlson \and Patrick Aboyoun}
\date{\today}

\begin{document} 

\maketitle

<<options,echo=FALSE>>=
options(width=72)
@

\section{Introduction}

This vignette focuses on common use cases involving the 
\Rpackage{GenomicRanges} package.

<<initialize, results=hide>>=
library(GenomicRanges)
@ 


\section{Chip-seq analysis}

This section presents an example workflow using a subset of the
ChIP-seq data for origin recognition complex (ORC) binding sites in
\textit{Saccharomyces cerevisiae} from the paper \textit{Conserved
nucleosome positioning defines replication origins}, Eaton et. al.
(PMID 20351051). The subset consists of all the MAQ alignments to
chromosome XIV for two replicates of ORC ChIP-seq data and is
contained in the Bioconductor package \Rpackage{EatonEtAlChIPseq}.
For illustration purposes we will focus on the first replicate.

<<EatonEtAlChIPseq>>=
library(EatonEtAlChIPseq)
data(orcAlignsRep1)
orcAlignsRep1
@

\subsection{Filtering alignments}

In this subsection we will demonstrate how to perform three filtering
operations on alignments produced by the MAQ software through the
following restrictions:

\begin{itemize}
\item Number of mismatches in alignment must be $\le$ 3 (guideline
  specified in paper)
\item No duplicates of \{chromosome, strand, position\} combinations
  (PCR bias correction)
\item An alignment on one strand must have a plausible alignment on
  the complementary strand (``pseudo" paired-read restriction)
\end{itemize} 

\noindent
The first two restrictions can be implemented using functionality from
the \Rpackage{ShortRead} package, while the last one can be performed
using operations within the \Rpackage{GenomicRanges} package.

In the previous section we loaded the \Robject{orcAlignsRep1} object,
an instance of the \Rpackage{ShortRead} class \Rclass{AlignedRead}.
This object contains information on the characteristics of the read as
well as its alignment to a reference genome, including information on
the number of mismatches for the best alignment. Using functionality from
the \Rpackage{ShortRead} package we can perform the first two filtering
operations, which result in a subset that is roughly 18\% of the size of
the original MAQ alignment file.

<<EatonAlignmentFiltering-1>>=
subsetRep1 <-
  orcAlignsRep1[alignData(orcAlignsRep1)[["nMismatchBestHit"]] <= 3L]
length(subsetRep1) / length(orcAlignsRep1)
subsetRep1 <- subsetRep1[occurrenceFilter(withSread=FALSE)(subsetRep1)]
length(subsetRep1) / length(orcAlignsRep1)
subsetRep1
@

The last filtering criterion, a ``pseudo" paired-read filter, requires
an understanding of the interval spans for the alignments rather than
just the ``leftmost" alignment location, i.e. start location on the
positive strand or end location on the negative strand, represented in
the \Rclass{AlignedRead} class. As such we will coerce the alignment
subset contained in \Robject{subsetRep1} to a \Rclass{GRanges} object
using a \Rmethod{coerce} method from the \Rpackage{ShortRead} package.

<<EatonOrcRanges>>=
rangesRep1 <- as(subsetRep1, "GRanges")
head(rangesRep1, 3)
@

\Rclass{AlignedRead} objects lack information on chromosome length,
so we will add it to the new \Robject{rangesRep1} object.

<<EatonAddSeqlengths>>=
seqlengths(rangesRep1) <- 784333
@

For our ``pseudo" paired-read filter, we will use the authors' estimate
of the mean fragment length (around 150 base pairs). In particular, we
will construct a filter where each alignment on the plus strand must have
a corresponding alignment somewhere within [100, 200] bp downstream on the
minus strand and vice versa with those alignments on the minus strand.

This filtering process can be achieved through an interval overlap
operation between the starts of the alignments on the minus strand
and the projected end of the alignments on the plus strand, where
the former can be derived by the code

<<EatonNegStarts>>=
negRangesRep1 <- rangesRep1[strand(rangesRep1) == "-"]
negStartsRep1 <- resize(negRangesRep1, 1)
@

\noindent
and the latter by the code

<<EatonPossibleEnds>>=
posRangesRep1 <- rangesRep1[strand(rangesRep1) == "+"]
posEndsRep1 <- shift(posRangesRep1, 99)
posEndsRep1 <- resize(posEndsRep1, 100)
strand(posEndsRep1) <- "-"
@

The results of the interval overlap are shown below. This filter
flagged roughly 4.5\% of the remaining alignments for removal,
resulting in keeping 17.3\% of the original set of alignments when
including the results of the first two filtering steps.

<<EatonMatchingPairs>>=
strandMatching <- findOverlaps(negStartsRep1, posEndsRep1)
posKeep <- unique(subjectHits(strandMatching))
negKeep <- unique(queryHits(strandMatching))
length(posKeep) / length(posEndsRep1)
length(negKeep) / length(negStartsRep1)
(length(posKeep) + length(negKeep)) / length(orcAlignsRep1)
@

<<EatonAlignmentFiltering-2>>=
posFilteredRangesRep1 <- posRangesRep1[posKeep]
negFilteredRangesRep1 <- negRangesRep1[negKeep]
@

\subsection{Finding peaks}

Once the alignments have been filtered, they can be aggregated into
coverage vectors. Assuming the size of the reads are uniformly
distributed within [100, 200] base pairs, we will generate a coverage
vector by giving full weight to alignments within 100 bp upstream and
then linearly decreasing weights for the next 100 bp upstream. For
the \Rfunction{runwtsum} function from \Rpackage{IRanges}, these weights
for the positive and negative strands are expressed by the vectors:

<<CoverageWeights, fig=TRUE, include=FALSE, eps=FALSE, height=3>>=
posWeights <- c(seq(0.01, 1, length = 100), rep(c(1, 0), c(101, 200)))
negWeights <- rev(posWeights)
plot(-200:200, posWeights, xlab = "Relative Position",
     ylab = "Coverage Weight", type = "l")
@

\begin{figure}[tb]
  \begin{center}
     \includegraphics[width=\textwidth]{GenomicRangesUseCases-CoverageWeights}  
     \caption{\label{fig-CoverageWeights}%
      Coverage weights for positive strand weighted sums.}
  \end{center}
\end{figure}

The first step in constructing this coverage vector is to tabulate
the alignments by their start positions on both the positive and
negative strand. We will use the \Rfunction{coverage} function on
these start values, which will produce \Rclass{RleList}
representations of the coverage vectors.

<<EatonStartCoverage>>=
posStartsCoverRep1 <- coverage(resize(posFilteredRangesRep1, 1))
negStartsCoverRep1 <- coverage(resize(negFilteredRangesRep1, 1))
@

\noindent
The second step in the process is aggregating upstream alignments
using the \Robject{posWeights} and \Robject{negWeights} objects
defined above.

<<EatonExtendedCoverage>>=
posExtCoverRep1 <-
  round(runwtsum(posStartsCoverRep1, k = 401, wt = posWeights,
                 endrule = "constant"))
negExtCoverRep1 <-
  round(runwtsum(negStartsCoverRep1, k = 401, wt = negWeights,
                 endrule = "constant"))
@

Before we proceeded any further, we will define two plot functions
for visualizing coverage vectors: \Rfunction{plotCoverage} for
displaying a single coverage vector and
\Rfunction{plotStrandedCoverage} for displaying back-to-back
coverage vectors for dual-stranded data.

<<plotCoverage>>=
plotCoverage <-
function(x, xlab = "Position", ylab = "Coverage",...)
{
    plot(c(start(x), length(x)), c(runValue(x), tail(runValue(x), 1)),
         type = "s", col = "blue", xlab = xlab, ylab = ylab, ...)
}
plotStrandedCoverage <-
function(positive, negative, xlab = "Position", ylab = "Coverage",...)
{
    ylim <- min(max(positive), max(negative)) * c(-1, 1)
    plotCoverage(positive, ylim = ylim, ...)
    lines(c(start(negative), length(negative)),
          - c(runValue(negative), tail(runValue(negative), 1)),
          type = "s", col = "red")
    abline(h = 0, col = "dimgray")
}
@

The coverage across chromosome XIV of the filtered alignements is
shown in Figure \ref{fig-EatonPlotStrands}. In general this plot
shows a near mirror image of coverage vectors between the positive
and negative strand.

<<EatonPlotStrands, fig=TRUE, include=FALSE, eps=FALSE, height=5>>=
plotStrandedCoverage(posExtCoverRep1[[1]], negExtCoverRep1[[1]])
@

\begin{figure}[tb]
  \begin{center}
     \includegraphics[width=\textwidth]{GenomicRangesUseCases-EatonPlotStrands}  
     \caption{\label{fig-EatonPlotStrands}%
      Plot of coverage across chromosome XIV.}
  \end{center}
\end{figure}

To reduce these measures to a single dimension, we will be
conservative and choose the smallest value between the positive
and negative strand coverage vectors using the \Rfunction{pmin}
method for \Rclass{RleList} objects.

<<EatonExtendedCoverage>>=
combExtCoverRep1 <- pmin(posExtCoverRep1, negExtCoverRep1)
quantile(combExtCoverRep1, c(0.5, 0.9, 0.95))
@

We now can call peaks off the combined coverage object
\Robject{combExtCoverRep1}. Since the median height for the
combined coverage on chromosome XIV is 4, we can limit our
attention to areas on the chromosome with coverage $\ge 5$
using the \Rfunction{slice} function. From there we can
derive a heuristic for a significant peaks as those achieving
a maximum height $\ge 28$, which selects 22 peaks.

<<EatonPeaks>>=
peaksRep1 <- slice(combExtCoverRep1, lower = 5)
peakMaxsRep1 <- viewMaxs(peaksRep1)
tail(sort(peakMaxsRep1[[1]]), 30)
peaksRep1 <- peaksRep1[peakMaxsRep1 >= 28]
peakRangesRep1 <-
  GRanges("chrXIV", as(peaksRep1[[1]], "IRanges"),
          seqlengths = seqlengths(rangesRep1))
length(peakRangesRep1)
@

We can now compare the significant peaks we selected with
those selected by the authors. Using interval comparison
tools, we see there is general agreement between our peaks
and those of the authors.

<<EatonExtendedReads>>=
data(orcPeaksRep1)
countOverlaps(orcPeaksRep1, peakRangesRep1)
countOverlaps(peakRangesRep1, orcPeaksRep1)
@


\section{Simple RNA-seq Analysis}

For the first example we will focus on RNA-seq analysis, but these
tools can also be applied to other kinds of short read data analysis.
It is also necessary to consider limitations imposed by BAM files and
the way that they are read in. BAM files can contain things that align
to multiple places (multi-reads) and this makes things more complex
since we don't know which things were aligned in this way. Therefore,
if these things are not filtered out beforehand by the aligner, we
will be counting some reads multiple times. Also, because alignments
consider the per base qualities of a sequence, it is also possible for
the same called sequence to be aligned onto two different places.
When we scan in the BAM file, these sequences will be read in based on
their predicted positions from the aligner. Thus, they can count as
different sequences even though they appear to be the same sequence
(based on their base pairs).

Let's begin by using the \Rpackage{Rsamtools} to retrieve some data
from a yeast experiment and load it into a \Rclass{GappedAlignments}
object. Here we will load in a single file that contains RNA-seq data
aligned from a wild type yeast experiment. This particular file only
has data from chromosome 13. While more data can be loaded, we will
limit our analysis to this subset for expediency purposes.

<<YeastData>>=
library(Rsamtools)
testFile <- system.file("bam", "isowt5_13e.bam", package="leeBamViews")
aligns <- readBamGappedAlignments(testFile)
@

At this point, we also have to consider carefully which build we have
so that we can know which annotation we need. This data is from Lee et
al (PMID 19096707), which is available in the \Rpackage{leeBamViews}
data package. This data originates from Gavin Sherlock's group at
Stanford and so naturally they designed their experiments with the SGD
genomes in mind. Therefore, it is probably reasonable to use the SGD
genome assembly data for \textit{Saccharomyces cerevisiae} when looking
at their data. This data is available from UCSC and is therefore also
accessible through the \Rpackage{rtracklayer}, \Rpackage{BSGenome} and
\Rpackage{GenomicFeatures} packages.


\subsection{Getting relevant genomic annotations}

Now that we have chosen a data source to match the data that the genes
were aligned to, we can simply get relevant annotation data to go with
it. The following code will load the \Rpackage{GenomicFeatures}
package, and then create an annotation database based on material from
the sgdGene track.

<<GetAnnoations>>=
library(GenomicFeatures)
txdb <- makeTranscriptDbFromUCSC(genome="sacCer2", tablename="sgdGene")
@

Then we simply have to retrieve data from the database using the
\Rfunction{exonsBy} method so that only exonic locations for each
transcript will be retrieved by setting the \Rfunarg{by} argument to
\Rcode{"tx"}.

<<exonsBy>>=
exonRanges <- exonsBy(txdb, "tx")
length(exonRanges)
exonRanges[1]
@

At this point, in spite of using the matching annotations for the data,
there is still a discrepancy in how the chromosomes were named from one
source to the next. In our data these all start with "Sc", whereas in
the annotations they do not. Also, numbers of the chromosomes for the
annotation are given in classic Roman numerals (as is traditional for
yeast). So let's recode the alignments to conform with the exon
annotations.

<<ammendData>>=
levels(rname(aligns))
levels(rname(aligns)) <-
  c(paste("chr", as.roman(1:16), sep=""), "chrM")
@


\subsection{Counting reads that overlap the annotations}

Now we can count how many reads mapped to each transcript.  And, since
the \Robject{exonRanges} object only has ranges for exons, this use of
\Rfunction{countOverlaps} will only consider the exon space when
calculating the counts for each.
<<count>>=
counts <- countOverlaps(exonRanges, aligns)
@

For simplicities sake, we will now calculate the RPKM.  This is not
meant as an endorsement of any one measure over any other, it's just
being used here as an example that users will probably have
encountered before. Using the annotation object, we can also get the
number of bases representing each \Robject{exonRanges} element.  Note
that this sum also only includes bases that are represented by the
exons from each transcript.
<<numBases>>=
numBases <- sum(width(exonRanges))
geneLengthsInKB <- numBases / 1000
@

And from this we could (as an example) calculate the sum of the
mappable reads and then calculate the RPKMs.
% RPKM as intrepreted from python code:
% RPKMs <- (counts/(sumMappable/1000000))/geneLengthsInKB
To do that we first would need to calculate the total number of reads
being considered by the experiment in millions of reads.
<<millionsMapped>>=
millionsMapped <- sum(counts) / 1000000
@

Then we have to calculate the RPM by considering the number of reads
for each transcript relative to this value.
<<RPM>>=
# counted reads / total reads in millions
rpm <- counts / millionsMapped
@

Finally, we calculate the RPKM by adjusting each RPM measurement for
the length of the transcript in Kb.
<<RPKM>>=
# reads per million per geneLength in Kb
rpkm <- rpm / geneLengthsInKB
@


Once we have calculated a measure of gene expression, we might find it
convenient to put it into a table as is expected by the
\Rpackage{DESeq} package.  To do this, we have to consider that we
will probably want to do just the above steps that collect the raw counts and then place those vectors into a
\Robject{data.frame} or \Robject{matrix} as a column.  For the sake of
saving space, lets assume you did that and created another vector
called \Robject{counts2}, then you could create your \Rpackage{DESeq}
object like this:

<<DESeqFrame, eval=FALSE>>=
deframe <- data.frame(counts, counts2)  
@

This will work in this case for as many other samples as you choose to
process because the countOvelaps method is always using the same
\Robject{exonRanges} object to quantify overlap regions with.




\subsection{Identifying and learning more about interesting transcripts} 

Now that we have calculated the RPKM, we can start to ask basic
questions about the results.  For example, which of the transcripts
had the two highest scores?  We can get this easily, because rpkm and
exonRanges will both be listed in the same order. 

<<sort>>=
sortedRPKM <- sort(rpkm)
highScoreGenes <- tail(sortedRPKM)
@

From the above output, notice how the names of the sortedRPKM are the
same as the index of exonRanges we need to retrieve?  We can therefore
see that transcript 6678 and 6676 are also the IDs of the transcript
with the highest RPKM. So we can use the \Rmethod{transcripts}
accessor learn which gene that transcript is associated with like
this: 

<<annotate1>>=
txs <- transcripts(txdb, 
                   vals=list(tx_id=names(highScoreGenes)), 
                   columns=c("tx_id","gene_id"))
systNames <- as.vector(unlist(elementMetadata(txs)["gene_id"]))
@

Once you know the gene id, you can use gene-centric annotation data
sources such as the \Rpackage{org.Sc.sgd.db} package
to learn more about it:

<<annnotate2>>=
library(org.Sc.sgd.db)
toTable(org.Sc.sgdGENENAME[systNames])
@

If you wish, you can also use these systematic names to replace the
rownames in any \Rpackage{DESeq} frame objects that you create.


% NEXT: Compete the subsection on finding UNKNOWN transcribed regions.
% Basically, 1) use the annotations to filter out reads from the aligned
% reads object, and then 2) use chipseq tools to do pileups, and then
% apply cutoffs to find putative transcribed regions.


\subsection{Identifying reads that do NOT overlap known annotation} 

At this point you might be satisfied with calculating the amount of
known genes in your model, but what if you wanted to know about
regions that were transcribed but which were NOT found amongst the
annotated transcripts?  To look at these you would first want to filter
out transcripts that are known from your dataset using
\Rfunction{subsetByOverlaps}.

<<filterKnowns>>=
filtData <- subsetByOverlaps(aligns, exonRanges)
length(filtData)
@

At this point, the \Robject{filtData} object only contains ranges that
did not overlap with any of the known exons from
\textit{Saccharomycess cerevisiae}. It is interesting to note that in
spite of having removed all known exons, this object still contains
about half of the original set of aligned reads.

If a coarser level of filtering is desired, you could also use the
output of \Rmethod{transcriptsBy} as the subject argument for
\Rmethod{subsetByOverlaps}.
Doing so would look like this:

<<filterKnowns2>>=
filtData2 <- subsetByOverlaps(aligns, transcriptsBy(txdb, "gene"))
length(filtData2)
@

In this specific case, you can see that not many additional things
have been filtered out by the coarser filtering which indicates that
not many of these unknown aligned reads are from intronic or
untranslated regions.  This makes sense, since introns in yeast tend
to be rare, infrequent and small.  Going forward in this example, we
will use the set that has only filtered out the exons just so that we
can look at the set of reads that is complementary to the ones we just
counted.

The next step in looking for transcripts that are unknown is to see
where the reads are located on the genome.  To do that we will use a
function originally developed for chip-seq analysis.  The
\Rfunction{coverage} method.

<<coverage>>=
cov <- coverage(filtData)
@

Since we only have data for chromosome 13, lets subset down to that.
Notice that in using the coverage method we lose the strand
information.  This is ok in this case, since the experiment has not
preserved the strand information anyhow.  If you had data that
measured each strand separately, then you would want to call coverage
twice at this point to keep the results separated.

<<coverageSubset>>=
cov <- cov[13]
@

And now we can slice that data so that we only consider islands where
things are continuously at least one read deep.

<<islands>>=
islands <- slice(cov, lower = 1)
@

Having identified those, we can then filter out just the islands where
the final width is at least 1000 bases long.

<<continous >1000>>=
transcribedRegions <- islands[width(islands) > 1000]
txr <- islands[width(islands) > 1000]
@

Finally, we should then be able to go back to the \Robject{BSGenome}
object and retrieve the actual sequences.  In order to recover these
sequences, we are going to specify that this data came from chromosome
13, and we will just retrieve the plus strand version of each (since
the experiment collapsed that data anyhow).

<<getSequence>>=
library(BSgenome.Scerevisiae.UCSC.sacCer2)
getYeastSequence <- function(data) {
  chr <- rep("chrXIII",length(start(data[[1]])))
  starts <- start(data[[1]])
  ends <- end(data[[1]])
  strands <- rep("+",length(start(data[[1]])))
  getSeq(Scerevisiae, names=chr, start=starts, end=ends, strand=strands)
}
DNASet <- DNAStringSet(getYeastSequence(transcribedRegions))
@



% <<translate>>=
% DNASet <- DNAStringSet(DNASet)
% translate(DNASet)
% @ 


% The next step after here is to compare these strings to the BSgenome and see if there are homologs, and if not, to then see if they are repeated etc.?




% Some other ideas: 
% Retrieve data from the \Rclass{GenomicFeatures} object for the range of
% a transcript.

% Convert that into a \Rclass{DNAStringSet} so that we can search a
% \Rclass{BSgenome} for the sequence (allowing for lots of gaps).

% See how many other instances of that are in a genome, pull those out,
% and then use \Rpackage{GenomicFeatures} again to identify those
% homologous ranges.

% \section{Getting data into a \Rclass{GenomeRanges}  object}
% \subsection{\Rclass{GenomeRanges} Objects} 



\section{Session Information}

The version number of R and packages loaded for generating the vignette were:

<<SessionInfo, echo=FALSE>>=
sessionInfo()
@

\end{document}
