Presentation given at VisBio workshop in Bergen, Norway.

1. Visual Analytics in omics - why, what, how?
Prof Jan Aerts
STADIUS - ESAT, Faculty of Engineering, University of Leuven, Belgium
Data Visualization Lab
jan.aerts@esat.kuleuven.be
jan@datavislab.org
creativecommons.org/licenses/by-nc/3.0/

2. • What problem are we trying to solve?
• What is Visual Analytics and how can it help?
• How do we actually do this?
• Some examples
• Challenges
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3. A. So what’s the problem?
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4. hypothesis-driven -> data-driven
Scientific Research Paradigms (Jim Gray, Microsoft)
I have an hypothesis -> need to generate data to (dis)prove it.
I have data -> need to find hypotheses that I can test.
1st 1,000s years ago empirical
2nd 100s years ago theoretical
3rd last few decades computational
4rd today data exploration
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5. What does this mean?
• immense re-use of existing datasets
• much of initial analysis is exploratory in nature => what’s my hypothesis?
• biologically interesting signals may be too poorly understood to be analyzed
in automated fashion
• visualization is very effective in facilitating human reasoning about complex
data
• automated algorithms often act as black boxes => biologists must have blind
faith in bioinformatician (and bioinformatician in his/her own skills)
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6. input
filter 1
filter 2
output A
filter 3
output B output
Opening the black box
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7. A B
C
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8. A B
C
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9. A B
C
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10. What’s my hypothesis?
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Martin Krzywinski

11. 11
Martin Krzywinski

12. 12
Martin Krzywinski

13. B. What is Visual Analytics and how can it help?
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14. 14

15. What is visualization?
T. Munzner
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16. What is visualization?
T. Munzner
cognition <=> perception
cognitive task => perceptive task
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17. • record information
• blueprints, photographs,
seismographs, ...
• analyze data to support reasoning
• develop & assess hypotheses
• discover errors in data
• expand memory
• find patterns (see Snow’s cholera map)
• communicate information
• share & persuade
• collaborate & revise
Why do we visualize data?
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18. pictorial superiority effect
“information”
“informa” “i”
65% 1%
72hr
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19. Steven’s psychophysical law
= proposed relationship between the magnitude of a physical stimulus and its
perceived intensity or strength
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20. Accuracy of quantitative perceptual tasks
McKinlay
what/where (qualitative)how much (quantitative)
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21. Accuracy of quantitative perceptual tasks
McKinlay
what/where (qualitative)how much (quantitative)
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22. Accuracy of quantitative perceptual tasks
McKinlay
“power of the plane”
what/where (qualitative)how much (quantitative)
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23. Pre-attentive vision
= ability of low-level human visual system to rapidly identify certain basic visual
properties
• some features “pop out”
• used for:
• target detection
• boundary detection
• counting/estimation
• ...
• visual system takes over => all cognitive power available for interpreting the
figure, rather than needing part of it for processing the figure
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24. 24

25. 25

26. 1. Combining pre-attentive features does not always work => would need to
resort to “serial search” (most channel pairs; all channel triplets)
e.g. is there a red square in this picture
Limitations of preattentive vision
2. Speed depends on which channel (use one that is good for
categorical; see further (“accuracy”))
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27. Gestalt laws - interplay between parts and the
whole
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28. Gestalt laws - interplay between parts and the
whole
• simplicity
• proximity
• similarity
• connectedness
• good continuation
• common fate
• familiarity
• symmetry
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29. Context affects perceptual tasks

30. C. How do we actually do this?
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31. Talking to domain experts
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32. Data visualization framework
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33. Card sorting
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34. Tools of the trade
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35. Processing - http://processing.org
• java
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36. D3 - http://d3js.org/
• javascript
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37. Vega - https://github.com/trifacta/vega/wiki
• html + json
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38. To use vega
• Create the json file
• Create the index.html
• Run “python -m SimpleHTTPServer”
• Go to http://127.0.0.1:8000/index.html
• Get help at https://github.com/trifacta/vega/wiki
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39. D. Examples
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40. HiTSee
Bertini E et al. IEEE Symposium on Biological Data Visualization (2011)
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41. Aracari
Ryo Sakai
Bartlett C et al. BMC Bioinformatics (2012)
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42. Meander
Pavlopoulos et al. Nucl Acids Res (2013)
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Georgios
Pavlopoulos

43. ParCoord
Boogaerts T et al. IEEE International Conference on
Bioinformatics & Bioengineering (2012)
Thomas Boogaerts
Endeavour gene prioritization
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44. Data filtering (visual parameter setting)
TrioVis
Ryo Sakai
Sakai R et al. Bioinformatics (2013)
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45. User-guided analysis
Spark
Nielsen et al. Genome Research (2012)
clustering
chromatin modification
DNA methylation
RNA-Seq
data samples
regions of interest
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46. Bret Victor - Ladder of abstration
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47. E. Challenges
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48. Many challenges remain
• scalability (data processing + perception), uncertainty, “interestingness”,
interaction, evaluation
• infrastructure & architecture
• fast imprecise answers with progressive refinement
• incremental re-computation
• steering computation towards data regions of interest
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49. Thank you
• Georgios Pavlopoulos
• Ryo Sakai
• Thomas Boogaerts
• Data Visualization Lab (datavislab.org)
• Erik Duval
• Andrew Vande Moere
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