1. - Visual Analytics -
The human back in the loop
Jan Aerts
Biodata Analysis and Visualization
Stadius Group, ESAT
Leuven University, Belgium
jan.aerts@esat.kuleuven.be
@jandot
http://orcid.org/0000-0002-6416-2717

2. 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

3. What does this mean?
• immense re-use of existing datasets
• much of initial analysis is exploratory in nature
• 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)

4. What is visualization?
T. Munzner

5. Data visualization framework

6. Data visualization framework
interactivity

7. Data visualization framework

8. Data visualization framework
visual
analytics infographics

11. “visual analytics”

12. • Types of interaction (Yi et al, IEEE Transactions on Visualization and Computer
Graphics, 2007)
• select -> mark something as interesting
• explore -> show me something else
• reconfigure -> show me a different arrangement
• encode -> show me a different representation
• abstract/elaborate -> show me less/more detail
• filter -> show me something conditionally
• connect -> show me connected items

14. Visualization for biological hypothesis generation
• example: eQTL data (IEEE BioVis visualization challenge 2011)
• 500 patients (affected + non-affected)
• 7500 SNPs; gene expression data for 15 genes
• PLINK one-locus/two-locus

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

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

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

19. when do I know that my algorithm is “correct”? -> peek into the black box
input
filter 1
filter 2
output A
filter 3
output B output C
Visualization for algorithm development

20. A
B
C

21. A
B
C

22. A
B
C

23. Caleydo
MatchMaker
Lex A et al. IEEE Transactions on
Visualization and Computer
Graphics (2010)

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

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

27. Visualization for (live) interaction with analysis
• alternating between visual and automatic methods -> continuous
refinement and verification of preliminary results
• misleading results: discovered at early stage
• leverage user’s (biologist’s) insights
• no black box

28. Cytoscape
Smoot et al. Bioinformatics (2011)

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

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

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

32. Goecks, J. et al. Nature Biotechnology (2012)
Galaxy Trackster
Goecks J et al. Nature Biotechnology (2012)

33. Bret Victor - Ladder of abstration

34. 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

35. Acknowledgments
• Bioinformatics Group at Stadius, Leuven University
• in particular: Ryo Sakai, Georgios Pavlopoulos
• visualization community for examples
• Jeremy for Trackster video