Point mark displays each data item as a symbol. Points are often used to create a scatter plot. In the genomic context, they could represent, for example, point mutations at genomic loci.
In addition to standard position channels and
strokeWidth channels, point mark has the following
The area of the point in pixels. In practice, the area is less because the shapes do not fill their rectangular container. Example: the diameter of a circle with the size of
100is 10 (sqrt(100)) pixels.
Gradient strength controls the amount of the gradient eye-candy effect. Valid values are between
Enables geometric zooming. The value is the base two logarithmic zoom level where the maximum point size is reached.
When a faceted visualization (Sample Track) has tens or hundreds of subgroups, the individual views may be smaller than the diameter of the point marks.
maxRelativePointDiameterproperty adjusts the scaling so that the largest possible point in the data is no larger than the specified fraction of the view height.
minAbsolutePointDiameterproperty works in concert with
maxRelativePointDiameter. The property specifies in pixels the absolute lower limit of the diameter of the largest possible point in the data.
Plenty of points¶
The example below demonstrates how points can be varied by using
Although points are infinitely small on the real number line, they have a specific diameter on the screen. Thus, closely located points tend to overlap each other. Decreasing the point size reduces the probability of overlap, but in a zoomed-in view, the plot may become overly sparse.
To control overplotting, the point mark provides two zooming behaviors that adjust the point size and visibility based on the zoom level.
Geometric zoom scales the point size down if the current zoom level is lower
than the specified level (bound).
geometricZoomBound mark property enables
geometric zooming. The value is the negative base two logarithm of the
relative width of the visible domain. Example:
0: (the default) full-size
points are always shown,
1: when a half of the domain is visible,
a quarter is visible, and so on.
The example below displays 200 000 semi-randomly generated points. The points reach their full size when 1 / 2^10.5 of the domain is visible, which equals about 1500X zoom.
You can use geometric zoom to improve rendering performance. Smaller points are faster to render than large points.
The score-based semantic zoom adjusts the point visibility by coupling a
score threshold to current zoom level. The
semanticScore channel enables
the semantic zoom and specifies the score field. The
property controls the fraction of data items to show in the fully zoomed-out
view, i.e., it specifies the threshold score. The fraction is scaled as the
viewport is zoomed. Thus, if the data is distributed roughly uniformly along
the zoomed axis, roughly constant number of points are visible at all zoom
levels. The score can be arbitrarily distributed, as the threshold is
computed using p-quantiles.
The example below has 200 000 semi-randomly generated points with a score. The scores are sampled from an exponential distribution. As the view is zoomed in, new points appear. Their number in the viewport stays approximately constant until the lowest possible score has been reached.
The score-based semantic zoom is great for filtering point mutations and indels that are scored using CADD, for example.