Visual Analytics in Omics: why, what, how?
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Visual Analytics in Omics: why, what, how?

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Presentation given at VisBio workshop in Bergen, Norway.

Presentation given at VisBio workshop in Bergen, Norway.

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Visual Analytics in Omics: why, what, how? Visual Analytics in Omics: why, what, how? Presentation Transcript

  • 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/
  • • 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
  • A. So what’s the problem? 3 View slide
  • 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 4 View slide
  • 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) 5
  • input filter 1 filter 2 output A filter 3 output B output Opening the black box 6
  • A B C 7
  • A B C 8
  • A B C 9
  • What’s my hypothesis? 10 Martin Krzywinski
  • 11 Martin Krzywinski
  • 12 Martin Krzywinski
  • B. What is Visual Analytics and how can it help? 13
  • 14
  • What is visualization? T. Munzner 15
  • What is visualization? T. Munzner cognition <=> perception cognitive task => perceptive task 16
  • • 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? 17
  • pictorial superiority effect “information” “informa” “i” 65% 1% 72hr 18
  • Steven’s psychophysical law = proposed relationship between the magnitude of a physical stimulus and its perceived intensity or strength 19
  • Accuracy of quantitative perceptual tasks McKinlay what/where (qualitative)how much (quantitative) 20
  • Accuracy of quantitative perceptual tasks McKinlay what/where (qualitative)how much (quantitative) 21
  • Accuracy of quantitative perceptual tasks McKinlay “power of the plane” what/where (qualitative)how much (quantitative) 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 23
  • 24
  • 25
  • 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”)) 26
  • Gestalt laws - interplay between parts and the whole 27
  • Gestalt laws - interplay between parts and the whole • simplicity • proximity • similarity • connectedness • good continuation • common fate • familiarity • symmetry 28
  • Context affects perceptual tasks
  • C. How do we actually do this? 30
  • Talking to domain experts 31
  • Data visualization framework 32
  • Card sorting 33
  • Tools of the trade 34
  • Processing - http://processing.org • java 35
  • D3 - http://d3js.org/ • javascript 36
  • Vega - https://github.com/trifacta/vega/wiki • html + json 37
  • 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 38
  • D. Examples 39
  • HiTSee Bertini E et al. IEEE Symposium on Biological Data Visualization (2011) 40
  • Aracari Ryo Sakai Bartlett C et al. BMC Bioinformatics (2012) 41
  • Meander Pavlopoulos et al. Nucl Acids Res (2013) 42 Georgios Pavlopoulos
  • ParCoord Boogaerts T et al. IEEE International Conference on Bioinformatics & Bioengineering (2012) Thomas Boogaerts Endeavour gene prioritization 43
  • Data filtering (visual parameter setting) TrioVis Ryo Sakai Sakai R et al. Bioinformatics (2013) 44
  • User-guided analysis Spark Nielsen et al. Genome Research (2012) clustering chromatin modification DNA methylation RNA-Seq data samples regions of interest 45
  • Bret Victor - Ladder of abstration 46
  • E. Challenges 47
  • 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 48
  • Thank you • Georgios Pavlopoulos • Ryo Sakai • Thomas Boogaerts • Data Visualization Lab (datavislab.org) • Erik Duval • Andrew Vande Moere 49