Aardvark uses composite visualizations of trees time series and images to help
researchers understand how cancer cells grow divide and spread.
Our collaborators collect time series microscopy
images where cells are tracked over time derived attributes such as cell mass
and cell location are measured resulting in time series data
and when cells divide the relationship is recorded to produce a tree or cell lineage
each of these data types represent a different aspect of the data
and visualizing them independently cannot tell the whole story
for instance, simply plotting the mass over time may show one cell growing
dividing into two, then four but what if the line chart shows an outlier
in this chart alone we cannot verif ver ify if this is a cell growing
abnormally large or an Imaging error to answer this we need to find the
corresponding cell in the images in another example we see a
divergence in the cell growth but again we cannot tell in
this view if the faster growing cell divides into the faster growing offspring
to do this we need to find the corresponding cells in a tree view
our collaborators are frequently crosschecking between visualization views
but this requires mentally linking separate visual data elements together
therefore our work focuses on easing this burden by showing
all three data types together in a composite visualization
but how do we actually combine these disparate types into a seamless visualization
let's look at our first composite visualization to explain our design principles
here we select trees as the primary data type which we show in a node-link representation
this visualization will then host our secondary data types
we can then choose a composition technique to embed the client visualization
in this case we nest the time series data inside each node
alternatively for images we superimpose them along the nodes automatically and on demand
in Aardvark we use horizon charts to represent time series data
image snippets of cells are superimposed above the horizon charts
we place images on the tree topology near division points
and where large changes occur in cell attributes as the size of the tree changes additional
cells are added or removed mousing over the horizon chart
shows thumbnails with a full cell boundary beyond the clipped region
hovering over a cell image shows the previous and next frames
and selecting it pins those images in this case the images reveal a missed division
the cell snippets show that the cell divided but one of the daughter cells is tracked
the second composite visualization uses the time series data as a primary data type
a line chart is well suited for comparing the development of multiple cells
the tree data is shown on demand through connecting lines and styling
images of cells are shown on demand above their corresponding data points
here each line represents the mass of a single cell over time
hovering over a line will show the cell at that point in time
selecting a cell will display connections explicitly with a dashed line and use this
color to differentiate cells in the two branches
here we can see a divergence in the growth between the green and violet branches
the final composite visualization uses images as the primary data type
showing the images directly helps reveal the spatial relationships between cells
cell positions over time are superimposed directly in this coordinate space
cell relationships are shown with a node-link representation
leaf nodes coincide with the cells in the image and internal nodes represent cell ancestors
in Aardvark we can see how this looks as one cell divides into two, then four, then eight
as the number of cells increases we can see when they begin to overlap,
resulting in issues with tracking
together these three views comprise Aardvark,
an open source visualization system for trees, time-series, and images
thanks for watching and remember with Aardvark the answer is just
a nose length away so keep on digging!