Visual Analytics in Omics - why, what, how?
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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.
Visual Analytics in Omics - why, what, how?
- 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
- 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
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- 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
- 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
- 39. Card sorting 39
- 40. Tools of the trade 40
- 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
- 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