Contents

1 Introduction

gmoviz logo

This vignette will guide you through the more advanced uses of gmoviz, such as the incremental apporach to generating plots and making finer modifications. It is highly recommended that you have read the basic overview of gmoviz before this vignette.

2 Incremental plotting steps

As well as high-level functions functions, gmoviz contains many lower-level functions that can be used to construct a plot track-by-track for more flexibility.

2.1 Dataset

This section will use the rBiocpkg("pasillaBamSubset") package for example data, so please ensure you have it installed before proceeding:

if (!require("pasillaBamSubset")) {
    if (!require("BiocManager"))
        install.packages("BiocManager")
    BiocManager::install("GenomicAlignments")
}
library(pasillaBamSubset)

2.2 Initialisation & Ideograms

The first step in creating a circular plot is to initialise it. This involves creating the ideogram (the rectangles that represent each sequence), which lays out the sectors for data to be plotted into. To do this, we need some ideogram data, in one of the following formats:

  • A GRanges, with one range for each sector you’d like to plot.
  • A data.frame, with three columns: chr (sector’s name), start and end.

For example, the following two ideogram data are equivalent:

ideogram_1 <- GRanges(seqnames = c("chrA", "chrB", "chrC"),
                 ranges = IRanges(start = rep(0, 3), end = rep(1000, 3)))
ideogram_2 <- data.frame(chr = c("chrA", "chrB", "chrC"), 
                    start = rep(0, 3),
                    end = rep(1000, 3))
print(ideogram_1)
#> GRanges object with 3 ranges and 0 metadata columns:
#>       seqnames    ranges strand
#>          <Rle> <IRanges>  <Rle>
#>   [1]     chrA    0-1000      *
#>   [2]     chrB    0-1000      *
#>   [3]     chrC    0-1000      *
#>   -------
#>   seqinfo: 3 sequences from an unspecified genome; no seqlengths
print(ideogram_2)
#>    chr start  end
#> 1 chrA     0 1000
#> 2 chrB     0 1000
#> 3 chrC     0 1000

Both of the higher level functions featureDiagram and insertionDiagram do this as their first step.

2.2.1 Reading in the ideogram data

Of course, typing this manually each time is troublesome. gmoviz provides the function getIdeogramData which creates a GRanges of the ideogram data from either a .bam file, single .fasta file or a folder containing many .fasta files.1 Note that reading in from a .bam file is significantly faster than from a .fasta file. This function can be used as follows:

## from a .bam file
fly_ideogram <- getIdeogramData(bam_file = pasillaBamSubset::untreated3_chr4())

## from a single .fasta file
fly_ideogram_chr4_only <- getIdeogramData(
  fasta_file = pasillaBamSubset::dm3_chr4())

But what if we wanted to read in just the chr3L? Luckily getIdeogramData has filters to select the specific sequences you want.

2.2.1.1 Filtering ideogram data

When reading in the ideogram data from file, there are often sequences in the .bam file or .fasta file folder that are not necessary for the plot. Thus, the getIdeogramData function provides three filters to allow you to only read in the sequences you want.2 These filters only work on the bam_file and fasta_folder input methods. Using a fasta_file means that filtering is not possible (although you can of course edit the ideogram GRanges after it is generated).

If we want only a single chromosome/sequence, we can supply it to wanted_chr:

getIdeogramData(bam_file = pasillaBamSubset::untreated3_chr4(),
                wanted_chr = "chr4")
#> GRanges object with 1 range and 0 metadata columns:
#>       seqnames    ranges strand
#>          <Rle> <IRanges>  <Rle>
#>   [1]     chr4 0-1351857      *
#>   -------
#>   seqinfo: 1 sequence from an unspecified genome; no seqlengths

Alternatively, if we want all chromosomes/sequences expect one, we can supply it to unwanted_chr:

getIdeogramData(bam_file = pasillaBamSubset::untreated3_chr4(),
                unwanted_chr = "chrM")
#> GRanges object with 7 ranges and 0 metadata columns:
#>       seqnames     ranges strand
#>          <Rle>  <IRanges>  <Rle>
#>   [1]    chr2L 0-23011544      *
#>   [2]    chr2R 0-21146708      *
#>   [3]    chr3L 0-24543557      *
#>   [4]    chr3R 0-27905053      *
#>   [5]     chr4  0-1351857      *
#>   [6]     chrX 0-22422827      *
#>   [7]  chrYHet   0-347038      *
#>   -------
#>   seqinfo: 7 sequences from an unspecified genome; no seqlengths

Finally, you can supply any regex pattern to just_pattern to create your own custom filter:

getIdeogramData(bam_file = pasillaBamSubset::untreated3_chr4(),
                just_pattern = "R$")
#> GRanges object with 2 ranges and 0 metadata columns:
#>       seqnames     ranges strand
#>          <Rle>  <IRanges>  <Rle>
#>   [1]    chr2R 0-21146708      *
#>   [2]    chr3R 0-27905053      *
#>   -------
#>   seqinfo: 2 sequences from an unspecified genome; no seqlengths

Of course, for these filters to work the spelling of the filter must exactly match the spelling of the .fasta file names or the sequences in the .bam file.

2.2.2 Initialising the graph

Now that we have the ideogram data, we can initialise the graph. For this example, we will just focus on chromosome 4.

We can see that a rectangle has been plotted and labelled to indicate chr4. Changing a few visual settings, we can create a better looking ideogram:

However, these small tweaks are not the only way we can enhance the appearance of our plot. gmovizInitialise can also display coverage data and labels, as well as supporting zooming and alteration of sector widths.

2.2.2.1 ‘Coverage rectangles’

As demonstrated with the insertionDiagram and featureDiagram functions, we can supply some coverage_data to enhance the ideogram and change the regular rectangles into line graphs which display the coverage (‘coverage rectangles’). This then allows the easy identification of deletions, duplications and other events which alter the coverage.

2.2.2.1.1 Reading in coverage data

To do this, we must first read in the coverage information from the .bam file. This can be done with the getCoverage function:

chr4_coverage <- getCoverage(
  regions_of_interest = "chr4", 
  bam_file = pasillaBamSubset::untreated3_chr4(),
  window_size = 350, smoothing_window_size = 400)

Here, we get the smoothed and windowed coverage for chr4.3 See below the section on smoothing and windowing for the effect of each of these arguments As we wanted the coverage for the entire chr4, we could simply make regions_of_interest = "chr4". However, we could also have supplied a GRanges describing that area instead. Whichever input is used, it is really important that the sequence names match exactly. For example, the following will t hrow an error, because there is no sequence named “4” or “Chr4” in the .bam file:

getCoverage(regions_of_interest = "4", 
            bam_file = pasillaBamSubset::untreated3_chr4(),
            window_size = 300, smoothing_window_size = 400)
#> Error in getCoverage(regions_of_interest = "4", bam_file = pasillaBamSubset::untreated3_chr4(), : Make sure all of the chromsomes in regions_of_interest are in
#>                 the bam file and spelled exactly the same as in the bam
getCoverage(regions_of_interest = "Chr4", 
            bam_file = pasillaBamSubset::untreated3_chr4(),
            window_size = 300, smoothing_window_size = 400)
#> Error in getCoverage(regions_of_interest = "Chr4", bam_file = pasillaBamSubset::untreated3_chr4(), : Make sure all of the chromsomes in regions_of_interest are in
#>                 the bam file and spelled exactly the same as in the bam
2.2.2.1.2 Plotting coverage

Now that we have the coverage data, we can plot the ideogram again using this information. To draw a ‘coverage rectangle’ we need to firstly specifiy the coverage_data to be used (as either a GRanges or a data frame) and then also supply to coverage_rectangle a vector of the sector names to plot the coverage data for.4 This means that you can have the coverage of multiple sequences/regions in the same GRanges but choose to plot only some of them.

As you can see, the chr4 ideogram rectangle is replaced with a line graph showing the coverage over the entire chromosome. The coloured area represents the coverage, allowing easy identification of high and low coverage areas.

2.2.2.1.3 Smoothing and windowing coverage data

When reading in the coverage data, there are two additional parameters window_size and smoothing_window_size that modify the values.

  • window_size controls the window size over which coverage is calculated (where a window size of 1 is per base coverage. A larger window size will reduce the time taken to read in, smooth and plot the coverage. It will also remove some of the variation in the coverage, although this is not its primary aim. If you have more than 10-15,000 points, it is highly recommended to use a larger window size, as this will take a long time to plot.

  • smoothing_window_size controls the window used for moving average smoothing, as carried out by the pracma package. It does not reduce the number of points and so offers no speed improvement (in fact, it increases the time taken to read in the coverage data). It does, however, reduce the variation to produce a smoother, more attractive plot.

For example, try running the following:

# default window size (per base coverage)
system.time({getCoverage(regions_of_interest = "chr4", 
                         bam_file = pasillaBamSubset::untreated3_chr4())})

# window size 100
system.time({getCoverage(regions_of_interest = "chr4", 
                         bam_file = pasillaBamSubset::untreated3_chr4(),
                         window_size = 100)})

# window size 500
system.time({getCoverage(regions_of_interest = "chr4", 
                         bam_file = pasillaBamSubset::untreated3_chr4(),
                         window_size = 500)})

Notice how going from the default window size of 1 (per base coverage) to a relatively modest window size of 100 dramatically reduces the time needed to read in the coverage data.

In terms of the appearance of the plot: (note: for speed, we will plot only a subset of the chromosome: from 70000-72000bp)

# without smoothing
chr4_region <- GRanges("chr4", IRanges(70000, 72000))
chr4_region_coverage <- getCoverage(regions_of_interest = chr4_region,
                          bam_file = pasillaBamSubset::untreated3_chr4())
gmovizInitialise(ideogram_data = chr4_region, coverage_rectangle = "chr4", 
                 coverage_data = chr4_region_coverage, custom_ylim = c(0,4))

# with moderate smoothing 
chr4_region_coverage <- getCoverage(regions_of_interest = chr4_region,
                          bam_file = pasillaBamSubset::untreated3_chr4(),
                          smoothing_window_size = 10)
gmovizInitialise(ideogram_data = chr4_region, coverage_rectangle = "chr4", 
                 coverage_data = chr4_region_coverage, custom_ylim = c(0,4))

# with strong smoothing
chr4_region_coverage <- getCoverage(regions_of_interest = chr4_region,
                          bam_file = pasillaBamSubset::untreated3_chr4(),
                          smoothing_window_size = 75)
gmovizInitialise(ideogram_data = chr4_region, coverage_rectangle = "chr4", 
                 coverage_data = chr4_region_coverage, custom_ylim = c(0,4))

Notice how adding smoothing dramatically improves the appearance of the plot. It also slightly reduces the time taken, because there are less extreme points. However, it does result in the loss of the finer detail of the coverage data. Thus, it is recommended that you play around with the values of smoothing_window_size and window_size and choose a value that is best suited to your own data.

2.2.2.2 Adding labels

One more functionality of gmovizInitialise is the ability to add labels to the outside of the plot. These can be used to identify regions of interest, such as genes or exons. The format of this should be:

  • A GRanges, with one range for each label & the label’s text as a metadata column label

  • A data.frame, with columns: chr (sector’s name), start and end that represent the position of the label and label that contains the label’s text

For example:

This is the same as how the labels in insertionDiagram and featureDiagram are implemented.

These labels can be manually specified as above, or read in from a .gff file, which also gives the option of colour coding the labels.5 This works simply by supplying a vector of colours (with the same length as the number of labels) to label_colour rather than just a single colour. You don’t have to have the colours as a part of the label data, it’s just a bit easier to keep track of that way. :

2.2.2.3 Changing sector sizes

By default, when using gmovizInitialise, each sector is sized to match its length relative to all of the other sectors on the plot to faciliate accurate representation of the scale. However, when a plot includes sectors that differ greatly in size, this can lead to problems. For example:

Notice that chr4 and chrYHet are much shorter than the other chromosomes. Thus, when we try to plot it, those three shorter sectors are so small that they are barely visible and their labels overlap leading to confusion.

We can deal with this in one of two ways: firstly by manually specifying the width (size) of each sector and secondly by zooming.

2.2.2.3.1 Setting custom sector widths

One way to manipulate the width/size of the sectors is to specify a custom_sector_width (custom sector width) vector. This vector should be the same length as the number of sectors. For example:

Notice that the custom_sector_width vector had length 7, because this is how many sectors there are. custom_sector_width can also be used for the insertionDiagram and featureDiagram functions in the same way.

2.2.2.3.2 Zooming

Whilst it is quite easy to set custom sector widths when there are only a few sectors, it can be quite troublesome for entire genomes. Also, using this method loses the relative sizing of all sectors, potentially leading to misinterpretation.

We can solve this problem by using the zooming functionality of gmovizInitialise. Doing this is relatively easy, all we need to do is supply the names of sector(s) to zoom to the zoom_sectors argument:

Now, chr4 and chrYHet are clearly visible alongside the rest of the sectors. Notice that chrYHet is still around 1/4 of the size of chr4, as is expected from their relative sizes (347038bp and 1351857bp, respectively). Also, all of the other chromosomes are still proportional. Another advantage of using the zooming is that the zoom_prefix applied to the start of the zoomed sector label makes it clear which sectors have been zoomed and which have not.

2.3 Adding tracks

After initialising the graph, the next step is to add tracks containing data. The two main types of track are the feature track and the numeric tracks, which can be combined as desired to create a customised plot.

2.3.1 Feature track

The ‘feature’ track, plots regions of interest just like the featureDiagram function (in fact, featureDiagram is just a convenient combination of gmovizInitialise and drawFeatureTrack). If you only want to plot features, then using featureDiagram is probably easier, but taking a track-by-track approach with drawFeatureTrack allows the combination of feature tracks with numeric data (see here for an example).

Just like featureDiagram, drawFeatureTrack requires feature_data. See here for an explanation of the format.

2.3.1.1 Reading in the feature data

Feature data can be read in from a .gff file using the getFeatures function.

features <- getFeatures(
  gff_file = system.file("extdata", "example.gff3", package = "gmoviz"), 
  colours = gmoviz::rich_colours)

Here, we have set the colours parameter to rich_colours, one of the five colour sets provided by gmoviz (see here for a description of each colour set) This means that the features will be allocated a colour from this set based on the ‘type’ field of the .gff file.

Once the feature data is read in, it is highly recommended to take a look and tweak it, if necessary.

2.3.1.2 Adding a feature track

Once we have the feature data, we can add a feature track to our plot. As we are only adding one track, increasing track_height to 0.18 gives us a bit more room to draw the features.

## remember to initialise first
gmovizInitialise(fly_ideogram_chr4_only, space_between_sectors = 25, 
                 start_degree = 78, xaxis_spacing = 30, sector_label_size = 1)
drawFeatureTrack(features, feature_label_cutoff = 80000, track_height = 0.18)

Notice that the geneY label was drawn inside the arrow whilst the others were drawn further into the circle. This is because we set feature_label_cutoff to 80000, so any features less than 80000bp long have their labels drawn outside, so that the label isn’t hanging off the end of the feature. See below for a detailed discussion of this concept.

2.3.1.3 Label plotting and cutoffs for features

When using the featureDiagram and drawFeatureTrack functions, you may have noticed that the position of the labels changes based on the size of the feature being plotted. For example, in the following plot, the second ‘ins’ label is drawn outside the feature, further towards the centre of the circle. This is because the size of the feature is less than the feature_label_cutoff.

Of course, you can specify your own cutoff. At 1, all labels will be plotted inside their respective features.

2.3.2 Numeric data tracks

As well as the feature track, gmoviz also contains more traditional numeric data tracks: the scatterplot and the line graph.

To showcase these tracks, we will generate some example data:

numeric_data <- GRanges(seqnames = rep("chr4", 50),
                       ranges = IRanges(start = sample(0:1320000, 50),
                                        width = 1),
                       value = runif(50, 0, 25))

Scatterplot tracks can be plotted with drawScatterplotTrack and line graphs with drawLinegraphTrack:

## remember to initialise first
gmovizInitialise(fly_ideogram_chr4_only, 
                 space_between_sectors = 25, start_degree = 78, 
                 sector_label_size = 1, xaxis_spacing = 30)
## scatterplot
drawScatterplotTrack(numeric_data)

## line graph
drawLinegraphTrack(sort(numeric_data), gridline_colour = NULL)

Note that for the line graph track, the data should be sorted in ascending order before plotting.

These numeric tracks can then be combined with feature tracks, as desired:

gmovizInitialise(fly_ideogram_chr4_only, space_between_sectors = 25, 
                 start_degree = 78, xaxis_spacing = 30, sector_label_size = 1)
drawScatterplotTrack(numeric_data, track_height = 0.14, yaxis_increment = 12)
drawFeatureTrack(features, feature_label_cutoff = 80000, track_height = 0.15)

2.4 Finishing touches

2.4.1 Legends

Like circlize, gmoviz relies on the package ComplexHeatmap (Gu, Eils, and Schlesner 2016) to generate its legends. More information about how this works can be found here, but for simplicity, gmoviz provides the makeLegends function to create legend objects without requiring an understanding of how the ComplexHeatmap package works.

Here, we will make a legend for the plot shown just previously.

legend <- makeLegends(
    feature_legend = TRUE, feature_data = features, 
    feature_legend_title = "Regions of interest", scatterplot_legend = TRUE,
    scatterplot_legend_title = "Numeric data", 
    scatterplot_legend_labels = "value")

legend is a legend object that can be plotted alongside a circos plot using the gmovizPlot function:

2.4.2 Arranging legends alongside plots

As explained here the legends of ComplexHeatmap are generated using grid graphics whilst the circular plots of circlize use base graphics. Thus, combining the two requires the use of the gridBase package. More information can be found at the aforementioned link, but gmoviz provides the gmovizPlot function to conveniently combine these two elements.

The gmovizPlot function generates a plot based on the code supplied to the plotting_functions parameter and saves it as an image, alongside and optional title and legend.6 The legend object can be either one generated using makeLegends or directly made using the functionality of the ComplexHeatmap package.

gmovizPlot(file_name = "example.svg", file_type = "svg", 
           plotting_functions = {
    gmovizInitialise(
        fly_ideogram_chr4_only, space_between_sectors = 25, start_degree = 78,
        xaxis_spacing = 30, sector_label_size = 1)
    drawScatterplotTrack(
        numeric_data, track_height = 0.14, yaxis_increment = 12)
    drawFeatureTrack(
        features, feature_label_cutoff = 80000, track_height = 0.15)
}, legends = legend, title = "Chromosome 4", background_colour = "white",
width = 8, height = 5.33, units = "in")
#> pdf 
#>   2

gmovizPlot example

gmovizPlot also supports .svg and .ps outputs, as well as .png. Using a vectorised output (.svg or .ps) is recommended as it allows you to easily edit the plot in Illustrator or similar software.

3 Other features and hints

3.1 gmoviz colour sets

Often 20+ sectors will be plotted during the initialisation of an entire genome. Thus, gmoviz includes five different colour sets each containing 34 colours in order to make it easier to give each of these sectors a unique, beautiful colour. Many of the colours in these sets are from or are heavily inspired by colorBrewer. The colour sets are:

  • nice_colours: The default colour set. Medium brightness, light colours designed for use on a white background.

  • pastel_colours: A set of subdued/pastel colours (a less saturated version of the nice_colours set), designed for use on a white backgorund.

  • rich_colours: A set of bright, vibrant colours (though not neon, like the bright_colours_transparent) designed for use on both white and black backgrounds.

  • bright_colours_transparent: A set of very bright/neon colours with slight transparency designed for use on a black background.

  • bright_colours_opaque: A set of very bright/neon colours without transparency designed for use on a black background.

Using bright_colours_transparent as the fill and bright_colours_opaque as the outline gives a nice effect on black backgrounds.

3.2 Adding to plots using circlize functions

As mentioned, gmoviz is based on the circlize (Gu et al. 2014) package by Zuguang Gu. Thus, circlize functions can be used alongside those from gmoviz to further customise plots.

Internally, gmoviz calls circos.clear() when initialising plots (at the beginning of the gmovizInitialise, featureDiagram and insertionDiagram functions) not at the end of functions. This means that, after you have run a gmoviz plotting function, you can use any circlize function to make further additions to the plot. For an example, we will further annotate the insertionDiagram plot produced in the basic overview vignette here:

## the data
example_insertion <- GRanges(seqnames = "chr12",
                      ranges = IRanges(start = 70905597, end = 70917885),
                      name = "plasmid", colour = "#7270ea", length = 12000,
                      in_tandem = 11, shape = "forward_arrow")

## the original plot
insertionDiagram(example_insertion, either_side = c(70855503, 71398284),
                 start_degree = 45, space_between_sectors = 20)

## annotate with text
circos.text(x = 81000, y = 0.25, sector.index = "plasmid", track.index = 1, 
            facing = "bending.inside", labels = "(blah)", cex = 0.75)

## annotate with a box
circos.rect(xleft = 0, xright = 12000, ytop = 1, ybottom = 0, 
            track.index = 2, sector.index = "plasmid", border = "red")

Of course, use of circlize functions is not just limited to small annotations. Functions such as circos.trackPlotRegion() and circos.track() can be used to add additional tracks to plots generated with gmoviz and likewise the gmoviz track functions (e.g. drawFeatureTrack) can be used to add to plots previously generated with circlize. For more information about using circlize, see the comprehensive book here

Warning: this also means that if you want to use circlize to generate a new plot after using gmoviz, you will need to use circos.clear() to reset.

Session Info

This vignette was rendered in the following environment:

#> R version 4.1.0 (2021-05-18)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows Server x64 (build 17763)
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#> [2] LC_CTYPE=English_United States.1252   
#> [3] LC_MONETARY=English_United States.1252
#> [4] LC_NUMERIC=C                          
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#> 
#> attached base packages:
#> [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
#> [8] methods   base     
#> 
#> other attached packages:
#>  [1] pasillaBamSubset_0.31.0 knitr_1.33              gmoviz_1.5.0           
#>  [4] GenomicRanges_1.45.0    GenomeInfoDb_1.29.0     IRanges_2.27.0         
#>  [7] S4Vectors_0.31.0        BiocGenerics_0.39.0     circlize_0.4.12        
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#> [59] SummarizedExperiment_1.23.0 zlibbioc_1.39.0            
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#> [63] blob_1.2.1                  crayon_1.4.1               
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#> [67] GenomicFeatures_1.45.0      hms_1.1.0                  
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#> [85] assertthat_0.2.1            cachem_1.0.5               
#> [87] gridBase_0.4-7              xfun_0.23                  
#> [89] pracma_2.3.3                restfulr_0.0.13            
#> [91] tibble_3.1.2                iterators_1.0.13           
#> [93] GenomicAlignments_1.29.0    AnnotationDbi_1.55.1       
#> [95] memoise_2.0.0               cluster_2.1.2              
#> [97] ellipsis_0.3.2

References

Gu, Zuguang, Roland Eils, and Matthias Schlesner. 2016. “Complex Heatmaps Reveal Patterns and Correlations in Multidimensional Genomic Data.” Bioinformatics.

Gu, Zuguang, Lei Gu, Roland Eils, Matthias Schlesner, and Benedikt Brors. 2014. “Circlize Implements and Enhances Circular Visualization in R.” Bioinformatics 30 (19): 2811–2. https://doi.org/10.1093/bioinformatics/btu393.