---
bibliography: ref.bib
---

# (PART) Workflows {-}

# H3K9ac, pro-B versus mature B

```{r, echo=FALSE, results='asis'}
library(rebook)
chapterPreamble()
```

## Overview

Here, we perform a window-based differential binding (DB) analysis to identify regions of differential H3K9ac enrichment between pro-B and mature B cells [@domingo2012].
H3K9ac is associated with active promoters and tends to exhibit relatively narrow regions of enrichment relative to other marks such as H3K27me3.
For this study, the experimental design contains two biological replicates for each of the two cell types.
We download the BAM files using the relevant function from the `r Biocpkg("chipseqDBData")` package.

```{r}
library(chipseqDBData)
acdata <- H3K9acData()
acdata
```

## Pre-processing checks 

### Examining mapping statistics

We use methods from the `r Biocpkg("Rsamtools")` package to compute some mapping statistics for each BAM file.
Ideally, the proportion of mapped reads should be high (70-80% or higher), 
while the proportion of marked reads should be low (generally below 20%).

```{r mapstat}
library(Rsamtools)
diagnostics <- list()
for (b in seq_along(acdata$Path)) {
    bam <- acdata$Path[[b]]
    total <- countBam(bam)$records
    mapped <- countBam(bam, param=ScanBamParam(
        flag=scanBamFlag(isUnmapped=FALSE)))$records
    marked <- countBam(bam, param=ScanBamParam(
        flag=scanBamFlag(isUnmapped=FALSE, isDuplicate=TRUE)))$records
    diagnostics[[b]] <- c(Total=total, Mapped=mapped, Marked=marked)
}

diag.stats <- data.frame(do.call(rbind, diagnostics))
rownames(diag.stats) <- acdata$Name
diag.stats$Prop.mapped <- diag.stats$Mapped/diag.stats$Total*100
diag.stats$Prop.marked <- diag.stats$Marked/diag.stats$Mapped*100
diag.stats
```

Note that all `r Biocpkg("csaw")` functions that read from a BAM file require BAM indices with `.bai` suffixes.
In this case, index files have already been downloaded by `H3K9acData()`, 
but users supplying their own files should take care to ensure that BAM indices are available with appropriate names.

### Obtaining the ENCODE blacklist 

We identify and remove problematic regions (Section \@ref(sec:problematic-regions)) using an annotated blacklist for the mm10 build of the mouse genome, 
constructed by identifying consistently problematic regions from ENCODE datasets [@dunham2012].
We download this BED file and save it into a local cache with the `r Biocpkg("BiocFileCache")` package.
This allows it to be used again in later workflows without being re-downloaded.

```{r}
library(BiocFileCache)
bfc <- BiocFileCache("local", ask=FALSE)
black.path <- bfcrpath(bfc, file.path("https://www.encodeproject.org",
    "files/ENCFF547MET/@@download/ENCFF547MET.bed.gz"))
```

Genomic intervals in the blacklist are loaded using the `import()` method from the `r Biocpkg("rtracklayer")` package.
All reads mapped within the blacklisted intervals will be ignored during processing in `r Biocpkg("csaw")` by specifying the `discard=` parameter (see below).

```{r}
library(rtracklayer)
blacklist <- import(black.path)
blacklist
```

### Setting up extraction parameters

We ignore reads that map to blacklist regions or do not map to the standard set of mouse nuclear chromosomes^[In this case, we are not interested in the mitochondrial genome, as these should not be bound by histones anyway.].

```{r, message=FALSE}
library(csaw)
standard.chr <- paste0("chr", c(1:19, "X", "Y"))
param <- readParam(minq=20, discard=blacklist, restrict=standard.chr)
```

Reads are also ignored if they have a mapping quality score below 20^[This is more stringent than usual, to account for the fact that the short reads ued here (32-36 bp) are more difficult to accurately align.].
This avoids spurious results due to weak or non-unique alignments that should be assigned low MAPQ scores by the aligner.
Note that the range of MAPQ scores will vary between aligners, so some inspection of the BAM files is necessary to choose an appropriate value.

## Quantifying coverage

### Computing the average fragment length

We estimate the average fragment length with cross correlation plots (Section \@ref(sec:ccf)).
Specifically, the delay at the peak in the cross correlation is used as the average length in our analysis (Figure \@ref(fig:h3k9ac-ccfplot)).

```{r}
x <- correlateReads(acdata$Path, param=reform(param, dedup=TRUE))
frag.len <- maximizeCcf(x)
frag.len
```

```{r h3k9ac-ccfplot, fig.cap="Cross-correlation function (CCF) against delay distance for the H3K9ac data set. The delay with the maximum correlation is shown as the red line."}
plot(1:length(x)-1, x, xlab="Delay (bp)", ylab="CCF", type="l")
abline(v=frag.len, col="red")
text(x=frag.len, y=min(x), paste(frag.len, "bp"), pos=4, col="red")
```

Only unmarked reads (i.e., not potential PCR duplicates) are used to calculate the cross-correlations.
However, general removal of marked reads is risky as it caps the signal in high-coverage regions of the genome.
Thus, the marking status of each read will be ignored in the rest of the analysis, i.e., no duplicates will be removed in downstream steps.

### Counting reads into windows

The `windowCounts()` function produces a `RangedSummarizedExperiment` object containing a matrix of such counts.
Each row corresponds to a window; each column represents a BAM file corresponding to a single sample^[Counting can be parallelized across files using the `BPPARAM=` argument.];
and each entry of the matrix represents the number of fragments overlapping a particular window in a particular sample. 

```{r}
win.data <- windowCounts(acdata$Path, param=param, width=150, ext=frag.len)
win.data
```

To analyze H3K9ac data, we use a window size of 150 bp.
This corresponds roughly to the length of the DNA in a nucleosome [@humburg2011], which is the smallest relevant unit for studying histone mark enrichment.
The spacing between windows is left as the default of 50 bp, i.e., the start positions for adjacent windows are 50 bp apart.

## Filtering windows by abundance

We remove low-abundance windows using a global filter on the background enrichment (Section \@ref(sec:global-filter)).
A window is only retained if its coverage is 3-fold higher than that of the background regions,
i.e., the abundance of the window is greater than the background abundance estimate by log~2~(3) or more.
This removes a large number of windows that are weakly or not marked and are likely to be irrelevant.

```{r}
bins <- windowCounts(acdata$Path, bin=TRUE, width=2000, param=param)
filter.stat <- filterWindowsGlobal(win.data, bins)
min.fc <- 3
keep <- filter.stat$filter > log2(min.fc)
summary(keep)
```

We examine the effect of the fold-change threshold in Figure \@ref(fig:h3k9ac-bghistplot).
The chosen threshold is greater than the abundances of most bins in the genome -- presumably, those that contain background regions.
This suggests that the filter will remove most windows lying within background regions.

```{r h3k9ac-bghistplot, fig.cap="Histogram of average abundances across all 2 kbp genomic bins. The filter threshold is shown as the red line."}
hist(filter.stat$filter, main="", breaks=50,
    xlab="Background abundance (log2-CPM)")
abline(v=log2(min.fc), col="red")
```

The filtering itself is done by simply subsetting the `RangedSummarizedExperiment` object.

```{r}
filtered.data <- win.data[keep,]
```

## Normalizing for trended biases

In this dataset, we observe a trended bias between samples in Figure \@ref(fig:h3k9ac-trendplot).
This refers to a systematic fold-difference in per-window coverage between samples that changes according to the average abundance of the window.

```{r h3k9ac-trendplot, fig.cap="Abundance-dependent trend in the log-fold change between two H3K9ac samples (mature B over pro-B), across all windows retained after filtering. A smoothed spline fitted to the log-fold change against the average abundance is also shown in red."}
win.ab <- scaledAverage(filtered.data)
adjc <- calculateCPM(filtered.data, use.offsets=FALSE)

logfc <- adjc[,4] - adjc[,1]
smoothScatter(win.ab, logfc, ylim=c(-6, 6), xlim=c(0, 5),
    xlab="Average abundance", ylab="Log-fold change")

lfit <- smooth.spline(logfc~win.ab, df=5)
o <- order(win.ab)
lines(win.ab[o], fitted(lfit)[o], col="red", lty=2)
```

To remove these biases, we use `r Biocpkg("csaw")` to compute a matrix of offsets for model fitting.

```{r}
filtered.data <- normOffsets(filtered.data)
head(assay(filtered.data, "offset"))
```

The effect of non-linear normalization is visualized with another mean-difference plot.
Once the offsets are applied to adjust the log-fold changes, the trend is eliminated from the plot (Figure \@ref(fig:h3k9ac-normplot)).
The cloud of points is also centred at a log-fold change of zero, indicating that normalization successfully removed the differences between samples. 

```{r h3k9ac-normplot, fig.cap="Effect of non-linear normalization on the trended bias between two H3K9ac samples. Normalized log-fold changes are shown for all windows retained after filtering. A smoothed spline fitted to the log-fold change against the average abundance is also shown in red."}
norm.adjc <- calculateCPM(filtered.data, use.offsets=TRUE)
norm.fc <- norm.adjc[,4]-norm.adjc[,1]
smoothScatter(win.ab, norm.fc, ylim=c(-6, 6), xlim=c(0, 5),
    xlab="Average abundance", ylab="Log-fold change")

lfit <- smooth.spline(norm.fc~win.ab, df=5)
lines(win.ab[o], fitted(lfit)[o], col="red", lty=2)
```

The implicit assumption of non-linear methods is that most windows at each abundance are not DB.
Any systematic difference between samples is attributed to bias and is removed.
The assumption of a non-DB majority is reasonable for this data set, given that the cell types being compared are quite closely related.

## Statistical modelling 

### Estimating the NB dispersion

First, we set up our design matrix.
This involves a fairly straightforward one-way layout with the groups representing our two cell types.

```{r}
celltype <- acdata$Description
celltype[grep("pro", celltype)] <- "proB"
celltype[grep("mature", celltype)] <- "matureB"

celltype <- factor(celltype)
design <- model.matrix(~0+celltype)
colnames(design) <- levels(celltype)
design
```

We coerce the `RangedSummarizedExperiment` object into a `DGEList` object (plus offsets) for use in `r Biocpkg("edgeR")`.
We then estimate the NB dispersion to capture the mean-variance relationship.
The NB dispersion estimates are shown in Figure \@ref(fig:h3k9ac-bcvplot) as their square roots, i.e., the biological coefficients of variation.
Data sets with common BCVs ranging from 10 to 20% are considered to have low variability for ChIP-seq experiments.

```{r}
library(edgeR)
y <- asDGEList(filtered.data)
str(y)
y <- estimateDisp(y, design)
summary(y$trended.dispersion)
```

```{r h3k9ac-bcvplot, fig.cap="Abundance-dependent trend in the BCV for each window, represented by the blue line. Common (red) and tagwise estimates (black) are also shown."}
plotBCV(y)
```

### Estimating the QL dispersion

We use quasi-likelihood methods to model window-specific variability, i.e., variance in the variance across windows.
However, with limited replicates, there is not enough information for each window to stably estimate the QL dispersion.
This is overcome by sharing information between windows with empirical Bayes (EB) shrinkage.
The instability of the QL dispersion estimates is reduced by squeezing the estimates towards an abundance-dependent trend (Figure \@ref(fig:h3k9ac-qlplot)).

```{r h3k9ac-qlplot, fig.cap="Effect of EB shrinkage on the raw QL dispersion estimate for each window (black) towards the abundance-dependent trend (blue) to obtain squeezed estimates (red)."}
fit <- glmQLFit(y, design, robust=TRUE)
plotQLDisp(fit)
```

The extent of shrinkage is determined by the prior degrees of freedom (d.f.).
Large prior d.f. indicates that the dispersions were similar across windows, such that stronger shrinkage to the trend could be performed to increase stability and power.

```{r}
summary(fit$df.prior)
```

Also note the use of `robust=TRUE` in the `glmQLFit()` call, which reduces the sensitivity of the EB procedures to outlier variances.
This is particularly noticeable in Figure \@ref(fig:h3k9ac-qlplot) with highly variable windows that (correctly) do not get squeezed towards the trend. 

### Examining the data with MDS plots

We use MDS plots to examine the similarities between samples. 
Ideally, replicates should cluster together while samples from different conditions should be separate.
While the mature B replicates are less tightly grouped, samples still separate by cell type in Figure \@ref(fig:h3k9ac-mdsplot).
This suggests that our downstream analysis will be able to detect significant differences in enrichment between cell types.

```{r h3k9ac-mdsplot, fig.cap="MDS plot with two dimensions for all samples in the H3K9ac data set. Samples are labelled and coloured according to the cell type."}
plotMDS(norm.adjc, labels=celltype,
    col=c("red", "blue")[as.integer(celltype)])
```

## Testing for DB 

Each window is tested for significant differences between cell types using the QL F-test.
For this analysis, the comparison is parametrized such that the reported log-fold change for each window represents that of the coverage in pro-B cells over their mature B counterparts.

```{r}
contrast <- makeContrasts(proB-matureB, levels=design)
res <- glmQLFTest(fit, contrast=contrast)
head(res$table)
```

We then control the region-level FDR by aggregating windows into regions and combining the $p$-values.
Here, adjacent windows less than 100 bp apart are aggregated into clusters.

```{r}
merged <- mergeResults(filtered.data, res$table, tol=100, 
    merge.args=list(max.width=5000))
merged$regions
```

A combined $p$-value is computed for each cluster using the method of @simes1986, based on the $p$-values of the constituent windows.
This represents the evidence against the global null hypothesis for each cluster, i.e., that no DB exists in any of its windows.
Rejection of this global null indicates that the cluster (and the region that it represents) contains DB.
Applying the BH method to the combined $p$-values allows the region-level FDR to be controlled.

```{r}
tabcom <- merged$combined
tabcom
```

We determine the total number of DB regions at a FDR of 5% 
by applying the Benjamini-Hochberg method on the combined $p$-values.

```{r}
is.sig <- tabcom$FDR <= 0.05
summary(is.sig)
```

Determining the direction of DB is more complicated, as clusters may contain windows that are changing in opposite directions.
One approach is to use the direction of DB from the windows that contribute most to the combined $p$-value, 
as reported in the `direction` field for each cluster.

```{r}
table(tabcom$direction[is.sig])
```

Another approach is to use the log-fold change of the most significant window as a proxy for the log-fold change of the cluster.

```{r}
tabbest <- merged$best
tabbest
```

In the table above, the `rep.test` column is the index of the window that is the most significant in each cluster, 
while the `rep.logFC` field is the log-fold change of that window.
We could also use this to obtain a summary of the direction of DB across all clusters.

```{r}
is.sig.pos <- (tabbest$rep.logFC > 0)[is.sig]
summary(is.sig.pos)
```

The final approach is generally satisfactory, though it will not capture multiple changes in opposite directions^[Try `mixedClusters()` to formally detect clusters that contain significant changes in both directions.].
It also tends to overstate the magnitude of the log-fold change in each cluster.

## Interpreting the DB results

### Adding gene-centric annotation

For convenience, we store all statistics in the metadata of a `GRanges` object.
We also store the midpoint and log-fold change of the most significant window in each cluster.

```{r}
out.ranges <- merged$regions
mcols(out.ranges) <- DataFrame(tabcom,
    best.pos=mid(ranges(rowRanges(filtered.data[tabbest$rep.test]))),
    best.logFC=tabbest$rep.logFC)
```

We can then use the built-in annotation function in `r Biocpkg("csaw")`  to report genic features overlapping to each region (Section \@ref(sec:detail-ranges)).
Annotated features that flank the region of interest are also reported.

```{r, message=FALSE}
library(org.Mm.eg.db)
library(TxDb.Mmusculus.UCSC.mm10.knownGene)
anno <- detailRanges(out.ranges, orgdb=org.Mm.eg.db,
    txdb=TxDb.Mmusculus.UCSC.mm10.knownGene)
head(anno$overlap)
head(anno$left)
head(anno$right)
```

The annotation for each region is stored in the metadata of the `GRanges` object.
The compact string form is useful for human interpretation, as it allows rapid examination of all genic features neighbouring each region.

```{r}
meta <- mcols(out.ranges)
mcols(out.ranges) <- data.frame(meta, anno)
```

### Using the `r Biocpkg("ChIPpeakAnno")` package

As its name suggests, the `r Biocpkg("ChIPpeakAnno")` package is designed to annotate peaks from ChIP-seq experiments [@zhu2010chippeakanno].
A `GRanges` object containing all regions of interest is supplied to the relevant function after removing all previous metadata fields to reduce clutter.
The gene closest to each region is then reported.
Gene coordinates are taken from the NCBI mouse 38 annotation, which is roughly equivalent to the annotation in the mm10 genome build.

```{r, message=FALSE}
library(ChIPpeakAnno)
data(TSS.mouse.GRCm38)
minimal <- out.ranges
elementMetadata(minimal) <- NULL
anno.regions <- annotatePeakInBatch(minimal, AnnotationData=TSS.mouse.GRCm38)
colnames(elementMetadata(anno.regions))
```

Alternatively, identification of all overlapping features within, say, 5 kbp can be achieved by setting `maxgap=5000` and `output="overlapping"` in `annotatePeakInBatch`.
This will report each overlapping feature in a separate entry of the returned `GRanges` object, i.e., each input region may have multiple output values.
In contrast, `detailRanges()` will report all overlapping features for a region as a single string, i.e., each input region has one output value.
Which is preferable depends on the purpose of the annotation -- the `detailRanges()` output is more convenient for direct annotation of a DB list, while the `annotatePeakInBatch()` output contains more information and is more convenient for further manipulation.

### Reporting gene-based results

Another approach to annotation is to flip the problem around such that DB statistics are reported directly for features of interest like genes.
This is more convenient when the DB analysis needs to be integrated with, e.g., differential expression analyses of matched RNA-seq data.
In the code below, promoter coordinates and gene symbols are obtained from various annotation objects.

```{r}
prom <- suppressWarnings(promoters(TxDb.Mmusculus.UCSC.mm10.knownGene,
    upstream=3000, downstream=1000, columns=c("tx_name", "gene_id")))
entrez.ids <- sapply(prom$gene_id, FUN=function(x) x[1]) # Using the first Entrez ID.
gene.name <- select(org.Mm.eg.db, keys=entrez.ids, keytype="ENTREZID", column="SYMBOL")
prom$gene_name <- gene.name$SYMBOL[match(entrez.ids, gene.name$ENTREZID)]
head(prom)
```

All windows overlapping each promoter are defined as a cluster.
We compute DB statistics are computed for each cluster/promoter using Simes' method, 
which directly yields DB results for the annotated features.
Promoters with no overlapping windows are assigned `NA` values for the various fields and are filtered out below for demonstration purposes.

```{r}
olap.out <- overlapResults(filtered.data, regions=prom, res$table)
olap.out
simple <- DataFrame(ID=prom$tx_name, Gene=prom$gene_name, olap.out$combined)
simple[!is.na(simple$PValue),]
```

Note that this strategy is distinct from counting reads across promoters.
Using promoter-level counts would not provide enough spatial resolution to detect sharp binding events that only occur in a subinterval of the promoter.
In particular, detection may be compromised by non-specific background or the presence of multiple opposing DB events in the same promoter.
Combining window-level statistics is preferable as resolution is maintained for optimal performance.

## Visualizing DB results

### Overview

We again use the `r Biocpkg("Gviz")` package to visualize read coverage across the data set at regions of interest [@hahne2016visualizing].
Coverage in each BAM file will be represented by a single track.
Several additional tracks will also be included in each plot.
One is the genome axis track, to display the genomic coordinates across the plotted region.
The other is the annotation track containing gene models, with gene IDs replaced by symbols (where possible) for easier reading.

```{r, message=FALSE}
library(Gviz)
gax <- GenomeAxisTrack(col="black", fontsize=15, size=2)
greg <- GeneRegionTrack(TxDb.Mmusculus.UCSC.mm10.knownGene, showId=TRUE,
    geneSymbol=TRUE, name="", background.title="transparent")
symbols <- unlist(mapIds(org.Mm.eg.db, gene(greg), "SYMBOL",
    "ENTREZID", multiVals = "first"))
symbol(greg) <- symbols[gene(greg)]
```

We will also sort the DB regions by p-value for easier identification of regions of interest.

```{r}
o <- order(out.ranges$PValue)
sorted.ranges <- out.ranges[o]
sorted.ranges
```

### Simple DB across a broad region

We start by visualizing one of the top-ranking DB regions.
This represents a simple DB event where the entire region changes in one direction (Figure \@ref(fig:simplebroadplot)).
Specifically, it represents an increase in H3K9ac marking at the *H2-Aa* locus in mature B cells.
This is consistent with the expected biology -- H3K9ac is a mark of active gene expression and MHCII components are upregulated in mature B cells [@hoffman2002changes].

```{r}
cur.region <- sorted.ranges[1]
cur.region
```

```{r, echo=FALSE, results="hide"}
if (cur.region!=GRanges("chr17", IRanges(34285101, 34290050))) {
    stop("first region does not match expectations")
}
```

One track is plotted for each sample, in addition to the coordinate and annotation tracks.
Coverage is plotted in terms of sequencing depth-per-million at each base.
This corrects for differences in library sizes between tracks.

```{r simplebroadplot, fig.width=8, fig.asp=0.75, fig.cap="Coverage tracks for a simple DB event between pro-B and mature B cells, across a broad region in the H3K9ac data set. Read coverage for each sample is shown as a per-million value at each base."}
collected <- list()
lib.sizes <- filtered.data$totals/1e6
for (i in seq_along(acdata$Path)) {
    reads <- extractReads(bam.file=acdata$Path[[i]], cur.region, param=param)
    cov <- as(coverage(reads)/lib.sizes[i], "GRanges")
    collected[[i]] <- DataTrack(cov, type="histogram", lwd=0, ylim=c(0,10),
        name=acdata$Description[i], col.axis="black", col.title="black",
        fill="darkgray", col.histogram=NA)
}
plotTracks(c(gax, collected, greg), chromosome=as.character(seqnames(cur.region)),
    from=start(cur.region), to=end(cur.region))
```

## Complex DB across a broad region

Complex DB refers to situations where multiple DB events are occurring within the same enriched region.
These are identified as those clusters that contain windows changing in both directions^[Technically, we should use `mixedClusters()` for rigorous identification of regions with significant changes in both directions. However, for simplicity, we'll just use a more _ad hoc_ approach here.].
Here, one of the top-ranking complex clusters is selected for visualization.

```{r}
complex <- sorted.ranges$num.up.logFC > 0 & sorted.ranges$num.down.logFC > 0
cur.region <- sorted.ranges[complex][2]
cur.region
```

```{r, echo=FALSE, results="hide"}
if (cur.region!=GRanges("chr5", IRanges(122987201, 122991450))) {
    stop("second region does not match expectations")
}
```

This region contains a bidirectional promoter where different genes are marked in the different cell types (Figure \@ref(fig:complexplot)).
Upon differentiation to mature B cells, loss of marking in one part of the region is balanced by a gain in marking in another part of the region.
This represents a complex DB event that would not be detected if reads were counted across the entire region.

```{r complexplot, fig.width=8, fig.asp=0.75, fig.cap="Coverage tracks for a complex DB event in the H3K9ac data set, shown as per-million values."}
collected <- list()
for (i in seq_along(acdata$Path)) {
    reads <- extractReads(bam.file=acdata$Path[[i]], cur.region, param=param)
    cov <- as(coverage(reads)/lib.sizes[i], "GRanges")
    collected[[i]] <- DataTrack(cov, type="histogram", lwd=0, ylim=c(0,3),
        name=acdata$Description[i], col.axis="black", col.title="black",
        fill="darkgray", col.histogram=NA)
}
plotTracks(c(gax, collected, greg), chromosome=as.character(seqnames(cur.region)),
    from=start(cur.region), to=end(cur.region))
```

### Simple DB across a small region

Both of the examples above involve differential marking within broad regions spanning several kilobases.
This is consistent with changes in the marking profile across a large number of nucleosomes.
However, H3K9ac marking can also be concentrated into small regions, involving only a few nucleosomes.
`r Biocpkg("csaw")` is equally capable of detecting sharp DB within these small regions.
This is demonstrated by examining those clusters that contain a smaller number of windows.

```{r}
sharp <- sorted.ranges$num.tests < 20
cur.region <- sorted.ranges[sharp][1]
cur.region
```

```{r, echo=FALSE, results="hide"}
if (cur.region!=GRanges("chr16", IRanges(36665551, 36666200))) {
    stop("second region does not match expectations")
}
```

Marking is increased for mature B cells within a 500 bp region (Figure \@ref(fig:simplesharpplot)), which is sharper than the changes in the previous two examples.
This also coincides with the promoter of the *Cd86* gene.
Again, this makes biological sense as CD86 is involved in regulating immunoglobulin production in activated B-cells [@podojil2003selective].

```{r simplesharpplot, fig.width=8, fig.asp=0.75, fig.cap="Coverage tracks for a sharp and simple DB event in the H3K9ac data set, shown as per-million values."}
collected <- list()
for (i in seq_along(acdata$Path)) {
    reads <- extractReads(bam.file=acdata$Path[[i]], cur.region, param=param)
    cov <- as(coverage(reads)/lib.sizes[i], "GRanges")
    collected[[i]] <- DataTrack(cov, type="histogram", lwd=0, ylim=c(0,3),
        name=acdata$Description[i], col.axis="black", col.title="black",
        fill="darkgray", col.histogram=NA)
}
plotTracks(c(gax, collected, greg), chromosome=as.character(seqnames(cur.region)),
    from=start(cur.region), to=end(cur.region))
```

Note that the window size will determine whether sharp or broad events are preferentially detected.
Larger windows provide more power to detect broad events (as the counts are higher), while smaller windows provide more resolution to detect sharp events.
Optimal detection of all features can be obtained by performing analyses with multiple window sizes and consolidating the results^[See `?consolidateWindows` and `?consolidateTests` for further information.], though -- for brevity -- this will not be described here.
In general, smaller window sizes are preferred as strong DB events with sufficient coverage will always be detected.
For larger windows, detection may be confounded by other events within the window that distort the log-fold change in the counts between conditions.

## Session information {-}

```{r, echo=FALSE, results='asis'}
rebook::prettySessionInfo()
```
