This document discusses visual analytics in omics data. It begins by noting the shift from hypothesis-driven to data-driven research due to large datasets. Visual analytics can help explore these data by opening the "black box" of algorithms and enabling researchers to develop hypotheses. Effective visualization leverages human perception through techniques like preattentive vision and Gestalt laws. Challenges to visual analytics include scalability issues for large datasets and identifying interesting patterns for further analysis. Examples demonstrate data exploration, filtering, and user-guided analysis in genomic applications.

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
2

3. A. What’s the problem?
3

4. hypothesis-driven -> data-driven
Scientific Research Paradigms (Jim Gray, Microsoft)
!
1st
1,000s years ago
empirical
!
2nd
100s years ago
theoretical
!
3rd
last few decades
computational
4rd
today
data exploration
!
I have an hypothesis -> need to generate data to (dis)prove it.
I have data -> need to find hypotheses that I can test.
4

5. What does this mean?
• immense re-use of existing datasets
• biologically interesting signals may be too poorly understood to be analyzed
in automated fashion
• much of initial analysis is exploratory in nature => what’s my hypothesis?
=> searching for unknown unknowns
• automated algorithms often act as black boxes => biologists must have blind
faith in bioinformatician (and bioinformatician in his/her own skills)
5

7. For domain expert: what’s my hypothesis?
Martin Krzywinski
7

8. For developer and domain expert:
opening the black box
input
filter 1
filter 2
filter 3
output A
output B
output C
8

9. B. What is Visual Analytics and how can it help?
9

10. Our research interest:
visual design + interaction design + backend
10

11. What is visualization?
visualization of simulations
in situ visualization
of real-world structures
11

12. What is visualization?
T. Munzner
12

13. What is visualization?
cognition <=> perception
cognitive task => perceptive task
T. Munzner
13

14. Why do we visualize data?
• 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
14

15. Sedlmair et al. IEEE Transactions on Visualization and Computer Graphics. 2012

16. The strength of visualization

17. pictorial superiority effect
“information”
72hr
“informa”
65%
“i”
10%
17

18. Steven’s psychophysical law
= proposed relationship between the magnitude of a physical stimulus and its
perceived intensity or strength
18

19. Accuracy of quantitative perceptual tasks
how much (quantitative)
what/where (qualitative)
McKinlay
19

20. Accuracy of quantitative perceptual tasks
how much (quantitative)
what/where (qualitative)
McKinlay
20

21. Accuracy of quantitative perceptual tasks
how much (quantitative)
what/where (qualitative)
“power of the plane”
McKinlay
21

22. 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
22

23. 23

24. 24

25. Limitations of preattentive vision
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
2. Speed depends on which channel (use one that is good for
categorical)
25

26. Gestalt laws - interplay between parts and the
whole
26

27. Gestalt laws - interplay between parts and the
whole
• simplicity
• familiarity
• proximity
• symmetry
• similarity
• connectedness
• good continuation
• common fate
27

28. Bret Victor - Ladder of abstration
28

29. For domain expert: what’s my hypothesis?
Martin Krzywinski
29

30. Martin Krzywinski
30

31. Martin Krzywinski
31

32. For developer and domain expert:
opening the black box
input
filter 1
filter 2
filter 3
output A
output B
output C
32

33. B
A
C
33

34. B
A
C
34

35. B
A
C
35

36. C. How do we actually do this?
36

37. Talking to domain experts
37

38. Data visualization framework
38

39. Card sorting
39

40. Tools of the trade
40

41. Processing - http://processing.org
• java
41

42. D3 - http://d3js.org/
• javascript
42

43. Vega - https://github.com/trifacta/vega/wiki
• html + json
43

44. D. Examples
Data exploration
Data filtering
User-guided analysis
44

45. Data exploration
HiTSee
Bertini E et al. IEEE Symposium on Biological Data Visualization (2011)

46. Aracari
Bartlett C et al. BMC Bioinformatics (2012)
Ryo Sakai
46

47. Reveal
Jäger, G et al. Bioinformatics (2012)

48. Meander
Pavlopoulos et al. Nucl Acids Res (2013)
Georgios
Pavlopoulos
48

49. ParCoord
Endeavour gene prioritization
Boogaerts T et al. IEEE International Conference on
Bioinformatics & Bioengineering (2012)
Thomas Boogaerts
49

50. Sequence logo

51. Seagull

53. subgroup
similarity
difference

54. Data filtering (visual parameter setting)
TrioVis
Sakai R et al. Bioinformatics (2013)
Ryo Sakai
54

55. User-guided analysis
clustering
regions of interest
Spark
Nielsen et al. Genome Research (2012)
data samples
chromatin modification
DNA methylation
RNA-Seq
55

56. BaobabView
decision trees
van den Elzen S & van Wijk J. IEEE Conference on
Visual Analytics Science and Technology (2011)

57. E. Challenges
57

58. 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
58

59. Computational scalability
• speed
• preprocessing big data: mapreduce = batch
• interactivity: max 0.3 sec lag!
• size
• multiple data resolutions => data size increase
• not all resolutions necessary for all data regions: steer computation to
regions of interest

60. • Options:
• distribute visualization calculations over cluster
• distributing scala/spark or other “real-time” mapreduce paradigm
• functional programming paradigm?
• lazy evaluation and smart preprocessing: only calculate what’s needed
=> generic framework

61. Perceptual scalability
• “overview first, then zoom and filter, details on demand”: breaks down with
very big datasets
• “analyze first, show results, then zoom and filter, details on demand” => need
to identify regions of interest and “interestingness features”
• identify higher-level structure in data (e.g. clustering, dimensionality
reduction) -> use these to guide user

62. Thank you
• Georgios Pavlopoulos
• Ryo Sakai
• Thomas Boogaerts
• Toni Verbeiren
• Data Visualization Lab (datavislab.org)
• Erik Duval
• Andrew Vande Moere
62