Gviz 1.30.3
Last edited: 23 April 2019
In order to make sense of genomic data one often aims to plot such
data in a genome browser, along with a variety of genomic annotation
features, such as gene or transcript models, CpG island, repeat
regions, and so on. These features may either be extracted from public
data bases like ENSEMBL or UCSC, or they may be generated or curated
in-house. Many of the currently available genome browsers do a
reasonable job in displaying genome annotation data, and there are
options to connect to some of them from within R
(e.g., using the
rtracklayer package). However, none of these solutions
offer the flexibility of the full R
graphics system to display large
numeric data in a multitude of different ways. The Gviz
package (Hahne and Ivanek 2016) aims to close this gap by providing a structured
visualization framework to plot any type of data along genomic
coordinates. It is loosely based on the GenomeGraphs
package by Steffen Durinck and James Bullard, however the complete
class hierarchy as well as all the plotting methods have been
restructured in order to increase performance and flexibility. All
plotting is done using the grid graphics system, and several
specialized annotation classes allow to integrate publicly available
genomic annotation data from sources like
UCSC or
ENSEMBL.
The fundamental concept behind the Gviz package is similar to the
approach taken by most genome browsers, in that individual types of
genomic features or data are represented by separate tracks. Within
the package, each track constitutes a single object inheriting from
class GdObject
, and there are constructor functions as well
as a broad range of methods to interact with and to plot these
tracks. When combining multiple objects, the individual tracks will
always share the same genomic coordinate system, thus taking the
burden of aligning the plot elements from the user.
It is worth mentioning that, at a given time, tracks in the sense of the
Gviz package are only defined for a single chromosome on a specific
genome, at least for the duration of a given plotting operation. You will later see
that a track may still contain information for multiple chromosomes,
however most of this is hidden except for the currently active
chromosome, and the user will have to explicitly switch the chromosome
to access the inactive parts. While the package in principle imposes
no fixed structure on the chromosome or on the genome names, it makes
sense to stick to a standardized naming paradigm, in particular when
fetching additional annotation information from online resources. By
default this is enforced by a global option
ucscChromosomeNames
, which is set during package loading and
which causes the package to check all supplied chromosome names for
validity in the sense of the UCSC definition (chromosomes have to
start with the chr
string). You may decide to turn this feature
off by calling options(ucscChromosomeNames=FALSE)
. For the
remainder of this vignette however, we will make use of the UCSC
genome and chromosome identifiers, e.g., the chr7
chromosome
on the mouse mm9
genome.
The different track classes will be described in more detail in the 4 section further below. For now, let’s just take a look at a typical Gviz session to get an idea of what this is all about. We begin our presentation of the available functionality by loading the package:
library(Gviz)
The most simple genomic features consist of start and stop
coordinates, possibly overlapping each other. CpG islands or
microarray probes are real life examples for this class of
features. In the Bioconductor world those are most often represented
as run-length encoded vectors, for instance in the IRanges
and GRanges
classes. To seamlessly integrate with other
Bioconductor packages, we can use the same data structures to generate
our track objects. A sample set of CpG island coordinates has been
saved in the cpgIslands
object and we can use that for our
first annotation track object. The constructor function
AnnotationTrack
is a convenient helper to create the
object.
library(GenomicRanges)
data(cpgIslands)
class(cpgIslands)
## [1] "GRanges"
## attr(,"package")
## [1] "GenomicRanges"
chr <- as.character(unique(seqnames(cpgIslands)))
gen <- genome(cpgIslands)
atrack <- AnnotationTrack(cpgIslands, name="CpG")
Please note that the AnnotationTrack
constructor (as most
constructors in this package) is fairly flexible and can accommodate
many different types of inputs. For instance, the start and end
coordinates of the annotation features could be passed in as
individual arguments start
and end
, as a
data.frame
or even as an IRanges
or
GRangesList
object. Furthermore, a whole bunch of coercion
methods are available for those package users that prefer the more
traditional R coding paradigm, and they should allow operations along
the lines of as(obj, 'AnnotationTrack')
. You may want to
consult the class’ manual page for more information, or take a look at
the 8 section for a listing of the
most common data structures and their respective counterparts in the
Gviz package.
With our first track object being created we may now proceed to the
plotting. There is a single function plotTracks
that
handles all of this. As we will learn in the remainder of this
vignette, plotTracks
is quite powerful and has a number of
very useful additional arguments. For now we will keep things very
simple and just plot the single CpG islands annotation track.
plotTracks(atrack)
As you can see, the resulting graph is not particularly spectacular. There is a title region showing the track’s name on a gray background on the left side of the plot and a data region showing the seven individual CpG islands on the right. This structure is similar for all the available track objects classes and it somewhat mimics the layout of the popular UCSC Genome Browser. If you are not happy with the default settings, the Gviz package offers a multitude of options to fine-tune the track appearance, which will be shown in the 3 section.
Apart from the relative distance of the CpG islands, this
visualization does not tell us much. One obvious next step would be to
indicate the genomic coordinates we are currently looking at in order
to provide some reference. For this purpose, the Gviz package offers
the GenomeAxisTrack
class. Objects from the class can be
created using the constructor function of the same name.
gtrack <- GenomeAxisTrack()
Since a GenomeAxisTrack
object is always relative to the
other tracks that are plotted, there is little need for additional
arguments. Essentially, the object just tells the
plotTracks
function to add a genomic axis to the
plot. Nonetheless, it represent a separate annotation track just as
the CpG island track does. We can pass this additional track on to
plotTracks
in the form of a list.
plotTracks(list(gtrack, atrack))
You may have realized that the genomic axis does not take up half of the available vertical plotting space, but only uses the space necessary to fit the axis and labels. Also the title region for this track is empty. In general, the Gviz package tries to find reasonable defaults for all the parameters controlling the look and feel of a plots so that appealing visualizations can be created without too much tinkering. However, all features on the plot including the relative track sizes can also be adjusted manually.
As mentioned before in the beginning of this vignette, a plotted track
is always defined for exactly one chromosome on a particular
genome. We can include this information in our plot by means of a
chromosome ideogram. An ideogram is a simplified visual representation
of a chromosome, with the different chromosomal staining bands
indicated by color, and the centromere (if present) indicated by the
shape. The necessary information to produce this visualization is
stored in online data repositories, for instance at UCSC. The Gviz
package offers very convenient connections to some of these
repositories, and the IdeogramTrack
constructor function is
one example for such a connection. With just the information about a
valid UCSC genome and chromosome, we can directly fetch the chromosome
ideogram information and construct a dedicated track object that can
be visualized by plotTracks
. Please not that you will need
an established internet connection for this to work, and that fetching
data from UCSC can take quite a long time, depending on the server
load. The Gviz package tries to cache as much data as possible to
reduce the bandwidth in future queries.
itrack <- IdeogramTrack(genome=gen, chromosome=chr)
Similar to the previous examples, we stick the additional track object into a list in order to plot it.
plotTracks(list(itrack, gtrack, atrack))
Ideogram tracks are the one exception in all of Gviz’s track objects in the sense that they are not really displayed on the same coordinate system like all the other tracks. Instead, the current genomic location is indicated on the chromosome by a red box (or, as in this case, a red line if the width is too small to fit a box).
So far we have only looked at very basic annotation features and how
to give a point of reference to our plots. Naturally, we also want to
be able to handle more complex genomic features, such as gene
models. One potential use case would be to utilize gene model
information from an existing local source. Alternatively, we could
download such data from one of the available online resources like UCSC
or ENSEBML, and there are constructor functions to handle these
tasks. For this example we are going to load gene model data from a
stored data.frame
. The track class of choice here is a
GeneRegionTrack
object, which can be created via the
constructor function of the same name. Similar to the
AnnotationTrack
constructor there are multiple possible ways
to pass in the data.
data(geneModels)
grtrack <- GeneRegionTrack(geneModels, genome=gen, chromosome=chr, name="Gene Model")
plotTracks(list(itrack, gtrack, atrack, grtrack))
In all those previous examples the plotted genomic range has been
determined automatically from the input tracks. Unless told otherwise,
the package will always display the region from the leftmost item to
the rightmost item in any of the tracks. Of course such a static view
on a chromosomal region is of rather limited use. We often want to
zoom in or out on a particular plotting region to see more details or
to get a broader overview. To that end, plotTracks
supports the from
and to
arguments that let us
choose an arbitrary genomic range to plot.
plotTracks(list(itrack, gtrack, atrack, grtrack), from=26700000, to=26750000)
Another pair of arguments that controls the zoom state are
extend.left
and extend.right
. Rather than from
and to
,
those arguments are relative to the
currently displayed ranges, and can be used to quickly extend the view
on one or both ends of the plot. In addition to positive or negative
absolute integer values one can also provide a float value between -1
and 1 which will be interpreted as a zoom factor, i.e., a value of 0.5
will cause zooming in to half the currently displayed range.
plotTracks(list(itrack, gtrack, atrack, grtrack), extend.left=0.5, extend.right=1000000)
You may have noticed that the layout of the gene model track has changed depending on the zoom level. This is a feature of the Gviz package, which automatically tries to find the optimal visualization settings to make best use of the available space. At the same time, when features on a track are too close together to be plotted as separate items with the current device resolution, the package will try to reasonably merge them in order to avoid overplotting.
Often individual ranges on a plot tend to grow quite narrow, in particular when zooming far out, and a couple of tweaks become helpful in order to get nice plots, for instance to drop the bounding borders of the exons.
plotTracks(list(itrack, gtrack, atrack, grtrack), extend.left=0.5, extend.right=1000000, col=NULL)
When zooming further in it may become interesting to take a look at
the actual genomic sequence at a given position, and the Gviz package
provides the track class SequenceTrack
that let’s you do just
that. Among several other options it can draw the necessary sequence
information from one of the BSgenome
packages.
library(BSgenome.Hsapiens.UCSC.hg19)
strack <- SequenceTrack(Hsapiens, chromosome=chr)
plotTracks(list(itrack, gtrack, atrack, grtrack, strack), from=26591822, to=26591852, cex=0.8)