VisWeek Presentation in the infovis session in 2012

1. Taxonomy-Based Glyph Design– with a Case Study on
Visualizing Workflows of Biological Experiments
Eamonn Maguire, Philippe Rocca-Serra, Susanna-Assunta Sansone, Jim Davies and Min Chen
University of Oxford, UK

2. The Road Map…
Aid quicker exploration and comparison of experimental workflows for
biologists performing experiments and curators who validate them.

3. Glyph: A glyph is a small visual object composed of a number
Getting there of visual channels which can be used independently as well as
constructively to depict attributes of a data record.
e.g. Chernoff Faces e.g. Glyph based on rectangles using color and
Image Sources: http://kspark.kaist.ac.kr/Human orientation.
%20Engineering.files/Chernoff/Chernoff%20Faces.htm
Healey, C et al. Perceptually-Based Brush Strokes for
Nonphotorealistic Visualization 2004

4. Some of the ~4000 concepts
labeling
But, we have a problem… nucleic acid extraction
hybridization
feature extraction
bioassay data transformation
Growth
We have 21,000 studies with > 500,000 individual cultured cells
saccharomyces cerevisiae scr101
experiments giving > 60 processes (actions on pool
materials) and >4000 inputs/outputs to those image acquisition
behavioral stimulus
processes. purify
pcr amplification
normalization
Creating 1000s of glyphs for each individual concept lowess group normalization
is simply not scalable. extraction
Scanning
feature extraction and analysis
We need a systematic process for glyph creation based immunoprecipitation
compound based treatment
on the properties of these concepts. transformation protocol
linear amplification
fresh frozen tissue
saccharomyces cerevisiae bqs252.
exponential growth in ypd.

5. Solution outline
Create a taxonomy Order Visual Channels… Map taxonomy to visual channels.
A structured hierarchical arrangement Color > shape > size > We can create a glyph for items
of concepts. orientation > texture. based on the position in taxonomy.
One or more concepts represented by
leaf nodes And provide design Higher levels in the taxonomy will
guidelines. command better visual channels.

6. Creating the taxonomy

7. Creating the taxonomy…input format
In each scheme, there are sub-classifications (4 in S1).
If a concept can be classified with this classification, it is assigned a 1, otherwise 0.

8. Creating the taxonomy…general workings
The algorithm runs recursively,
selecting each best scheme S
and attempting to sub-classify
each classification C
But how do we select the best
scheme?

9. Metric 1: Coverage
100% coverage yields value of 1
The more concepts a scheme can classify, the better.

10. Metric 2: Potential Use
Higher occurrence yields value closer to 1

11. Metric 3: Sub tree balance
Low standard deviation in number of concepts in each classification yields value closer to 1
A balanced tree is desirable and prevents a tree from having excessive height (greater height = need for more visual channels).

12. Metric 4: Number of Subclasses
Low number of classes yields value close to 1
Schemes with a high number of subclasses are penalized since a high number of subclasses would mean a high number of
levels to map to with the selected visual channels.
Only consider subclasses that are used.

13. Application to our case study
We have 8 schemes shown here, focusing mainly on processes.

14. Application to our case study
We have 21,000 biological studies with > 500,000 individual
experiments giving > 60 processes (actions on materials) and
>4000 inputs/outputs to those processes.
1. Concepts were extracted
2. Categories were created by a domain expert.
3. Taxonomy algorithm applied.
4. Taxonomy on the right created >>
Next we attempt to order visual channels and
create design guidelines.

15. Guidelines for design

16. Ordering Visual Channels

17. Bertin’s Visual Channels

18. Associative Selective Ordered Quantitative
facilitate grouping of all facilitate selection of one facilitate visual permits extraction of ratios
elements of a variable category of data and ignore ranking of data: without the need to inspect a
despite differing values: others: legend:
texture, color, orientation texture, color, orientation, texture, color & planar & size.
and shape. shape, planar, size & size.
brightness.

19. Pop-out effect
(Williams 67, Duncan 89, Luck 94, Bertin 83, Green 98, Wolfe 89, Treisman 77, Palmer 77, Parkhurst 02)

20. Visual Hierarchy
In particular we look at:
1.top-down (global);
2.salient feature detection of edges, points and colors.
Since they are most relevant to overview level processing of a scene.
[Palmer 77, Navon 77, Shor 71, Love 99, Kinchla 79]

21. Metaphor is important!
Material Combination Material Amplification Material Separation Material Collection

22. From Taxonomy to Visual Channels

23. Visual Mapping
Select design options based on the guidelines and the
level of the classification in the taxonomy and map the
scheme to selected Visual Channels and structure
C1 C3 C2
In Vitro In Vivo In Silico

24. Visual Mapping

25. Crush test.
Schemes high up in the taxonomy should be distinguishable at low resolution...overview level.
We should be able to distinguish high-
level classes in the taxonomy even at
low resolutions.

26. Implementation & Dissemination
Towards interoperable bioscience data
Sansone et al, 2012
Nature Genetics

27. Contributions
1. Systematic Approach For Glyph Design
• Ordering of concepts
• Ordering of visual channels according to
psychological literature
• Mapping between them
2. Application
• Biological Metadata
• Biological Workflows

28. Questions?
Funders
Thanks to the organizers and everyone here for listening!