Chapter 9 Create plotting regions for genomic data

Tracks are created and graphics are added by circos.genomicTrackPlotRegions(), or the short version circos.genomicTrack(). In following examples, chromosome will be used as the genomic category, and we assume data is simply a data frame in BED format (where the first column is the chromosome name, the second and third column are start and end positions, and the following columns are associated values). For more complex form of data and behaviour of the functions, we will introduce in Chapter 10.

Similar as circos.track(), circos.genomicTrack() also accepts a self- defined function which is applied in every cell but with different form.

circos.genomicTrackPlotRegion(data, = function(region, value, ...) {
    circos.genomicPoints(region, value, ...)

Inside, users can use low-level graphic functions to add basic graphics in each cell. expects two arguments region and value which are automatically processed and passed from circos.genomicTrack(). region is a two-column data frame which only contains start position and end position in the current chromosome. value is also a data frame which contains other columns (start for the fourth column, if it exists). Thus, basically, region can be thought as values on x axes and value as values on y axes.

There should be a third arguments ... which is mandatory and is used to pass user- invisible variables to inner functions and make magics (explained in Chapter 10). So whenever you use in circos.genomicTrack(), please add it to the end of your function.

Following code demonstrates the values for region and value when used inside

bed = generateRandomBed(nc = 2)
head(bed, n = 2)
##    chr  start    end     value1      value2
## 1 chr1  76226  90154 -0.3713480 -0.67590660
## 2 chr1 182930 216965 -0.4558215  0.05849737
circos.initializeWithIdeogram(plotType = NULL)
circos.genomicTrackPlotRegion(bed, = function(region, value, ...) {
    if(CELL_META$sector.index == "chr1") {
        print(head(region, n = 2))
        print(head(value, n = 2))
##    start    end
## 1  76226  90154
## 2 182930 216965
##       value1      value2
## 1 -0.3713480 -0.67590660
## 2 -0.4558215  0.05849737

Since circos.genomicTrack() creates a new track, it needs values to calculate data ranges on y direction. Users can either specify the index of numeric columns in data by numeric.column (named index or numeric index, it can also be a vector with more than one columns) or directly set ylim. If none of them are set, the function will try to look for all numeric columns in data (of course, excluding the first three columns), and set them as numeric.column.

circos.genomicTrackPlotRegion(data, ylim = c(0, 1), = function(region, value, ...) {
        circos.genomicPoints(region, value, ...)
circos.genomicTrackPlotRegion(data, numeric.column = c("value1", "value2"), = function(region, value, ...) {
        circos.genomicPoints(region, value, ...)

Since genomic functions are implemented by basic circlize functions, you can use anywhere to get information of sectors and tracks.

As you already see in previous examples, circlize also provides low-level graphic functions specifically designed for genomic data. They are all implemented by corresponding normal circlize functions, but needs input variables with special format.

In this chapter, we introduce the basic usage of circos.genomicTrack() and low-level circos.genomic*(). In Chapter 10, we will introduce more usages of these functions, which are especially designed for genomic regions measured at multiple conditions. Example plots are shown together in Chapter 10.

9.1 Points

Usage of circos.genomicPoints() is similar as circos.points(). circos.genomicPoints() expects a two-column data frame which contains genomic regions and a data frame containing corresponding values. Points are always drawn at the middle of each region. The data column of the y values for plotting should be specified by numeric.column. If numeric.column has length larger than one, all the specified columns will be used for adding points.

If the function is called inside circos.genomicTrack() and users have been already set numeric.column in circos.genomicTrack(), proper value of numeric.column will be passed to circos.genomicPoints() through ... in, which means, you must add ... as the final argument in circos.genomicPoints() to get such information. If numeric.column is not set in both places, circos.genomicPoints() will use all numeric columns detected in value.

Note here numeric.column is measured in value while numeric.column in circos.genomicTrack() is measured in the complete data frame. There is a difference of 3 for the column index! When numeric.column is passed to circos.genomicPoints() internally, 3 is subtracted automatically. If you use character index instead of numeric index, you do not need to worry about it.

Possible usages of circos.genomicPoints() are as follows.

circos.genomicPoints(region, value, numeric.column = c(1, 2))
circos.genomicPoints(region, value, cex, pch)
circos.genomicPoints(region, value, sector.index, track.index)
circos.genomicTrack(data, numeric.column = 4, = function(region, value, ...) {
        # numeric.column is automatically passed to `circos.genomicPoints()`
        circos.genomicPoints(region, value, ...)

If there is only one numeric column, graphical parameters such as pch, cex can be of length one or number of rows of region. If there are more than one numeric columns specified, points for each numeric column will be added iteratively, and the graphical parameters should be either length one or number of numeric columns specified.

circos.genomicPoints() is simply implemented by circos.points(). The basic idea of the implementation is shown as following code, so, if you don’t like the circos.genomic*() functions, it would not be difficult to directly use the circos.*() functions.

circos.genomicPoints = function(region, value, numeric.column = 1, ...) {
    x = (region[[2]] + region[[1]])/2
    for(i in numeric.column) {
        y = value[[i]]
        circos.points(x, y, ...)

9.2 Lines

circos.genomicLines() is similar as circos.lines(). The setting of graphical parameters is similar as circos.genomicPoints().

circos.genomicLines(region, value, ...)
circos.genomicLines(region, value, numeric.column = c(1, 2))
circos.genomicLines(region, value, area, baseline, border)
circos.genomicLines(region, value, sector.index, track.index)

circlize additionally provides a new option segment for lty by which each genomic regions represent as ‘horizontal’ lines at y positions (see Figure 10.2, track H).

circos.genomicLines(region, value, lwd, lty = "segment")

9.3 Text

For circos.genomicText(), the position of text can be specified either by numeric.column or a separated vector y. The labels of text can be specified either by labels.column or a vector labels.

circos.genomicText(region, value, ...)
circos.genomicText(region, value, y = 1, labels)
circos.genomicText(region, value, numeric.column, labels.column)
circos.genomicText(region, value, facing, niceFacing, adj)
circos.genomicText(region, value, sector.index, track.index)

9.4 Rectangles

For circos.genomicRect(), Since the left and right of the rectangles are already determined by the start and end of the genomic regions, we only need to set the positions of top and bottom of the rectangles by specifying ytop, ybottom or ytop.column, ybottom.column.

circos.genomicRect(region, value, ytop = 1, ybottom = 0)
circos.genomicRect(region, value, ytop.column = 2, ybottom = 0)
circos.genomicRect(region, value, col, border)

9.6 Mixed use of general circlize functions is applied on each cell, which means, besides genomic graphic functions, you can also use general circlize functions to add more graphics. For example, some horizontal lines and texts are added to each cell and axes are put on top of each cell.

circos.genomicTrack(bed, ylim = c(-1, 1), = function(region, value, ...) {
        circos.genomicPoints(region, value, ...)
        for(h in c(-1, -0.5, 0, 0.5, 1)) {
            circos.lines(CELL_META$cell.xlim, c(0, 0), lty = 2, col = "grey")
        circos.text(x, y, labels)