Data Visualization in Biomedical Sciences: More than Meets the Eye

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In science, data visualization serves two primary purposes. The first is to explore data sets interactively and the second is to communicate discoveries. However, the requirements for visualizations employed in these activities are very different. Therefore, the software tools used for these purposes are typically disconnected, creating significant challenges for reproducibility and effective communication of discoveries in data-driven biomedical science. In this presentation, I will address how a new approach to creating data visualization tools can connect data analysts and other stakeholders inside and outside the scientific community. I will introduce and demonstrate the "Vistories" approach that was motivated by these question. Presented at the 5th Cancer Research UK Big Data Analytics Conference on Data Visualization.

Science

http://gehlenborglab.org
Research Data Clinical Data

http://gehlenborglab.org
Research Data Clinical Data
Casual Users
Expert Users

http://gehlenborglab.org
Clinical Data
Casual Users
Expert Users
Research Data

http://higlass.io http://hipiler.higlass.io
http://caleydo.org/tools/stratomex http://halyos.gehlenborglab.org
https://vcg.github.io/upset
OncoThreads
http://gehlenborglab.org

REPORT
experiment
DATA
INSIGHT HYPOTHESIS
interpretation
hypothesis
generation

REPORT
experiment
DATA
INSIGHT HYPOTHESIS
interpretation
hypothesis
generation
EXPLANATION
“Storytelling”

REPORT
experiment
DATA
INSIGHT HYPOTHESIS
interpretation
hypothesis
generation
EXPLORATION
EXPLANATION
“Storytelling”
“Pattern Discovery”

REPORT
experiment
DATA
INSIGHT HYPOTHESIS
interpretation
HYPOTHESIS
hypothesis
generation
EXPLORATION
EXPLANATION
“Storytelling”
“Pattern Discovery”
HYPOTHESIS-DRIVEN DISCOVERY

REPORT
experiment
DATA
INSIGHT HYPOTHESIS
interpretation
DATA
hypothesis
generation
EXPLORATION
EXPLANATION
“Storytelling”
“Pattern Discovery”
DATA-DRIVEN DISCOVERY

Adobe IllustratorCytoscape
Insight
EXPLORATION EXPLANATION

Researchers Researchers
Decision Makers
Policy Makers
Citizens
…
Insight
EXPLORATION EXPLANATION
Decision Makers
Policy Makers
Citizens
…

Nature asked 1,576 researchers if there
is a reproducibility crisis in science.
M Baker, Nature 533, 452-454, 2016

0% 100%
No crisis (3%)
Don’t know (7%)
Slight crisis (38%)
M Baker, Nature 533, 452-454, 2016
Signiﬁcant crisis (52%)
Nature asked 1,576 researchers if there
is a reproducibility crisis in science.

Intentional?
Inability to capture everything?
Inability to communicate everything?
M Baker, Nature 533, 452-454, 2016

Intentional?
Inability to capture everything?
Inability to communicate everything?
SOCIAL ISSUE
TECHNICAL ISSUES
M Baker, Nature 533, 452-454, 2016

HOW IS THIS RELATED TO
DATA VISUALIZATION?

Tumor Subtypes
PROBLEM 1
Visualize overlap of patient sets across two or more stratifications.
PROBLEM 2
Visualize characteristics of patient sets within a stratification of interest.

M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, P Park, N Gehlenborg , Nature Methods (2014)
StratomeX

Is there a mutation that overlaps with this mRNA cluster?
Is there a CNV that affects survival?
Is there a pathway that is enriched in this cluster?
Is there a mutually exclusive mutation?
Query
Stratifications
Clinical Params
Pathways
Guided
Exploration
M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, P Park, N Gehlenborg , Nature Methods (2014)

Query
Rank
Visualize
Stratifications
Clinical Params
Pathways
Guided
Exploration
M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, P Park, N Gehlenborg , Nature Methods (2014)

DATA-DRIVEN DISCOVERY
REPORT
experiment
DATA
INSIGHT HYPOTHESIS
interpretation
DATA
hypothesis
generation
EXPLORATION
EXPLANATION
“Storytelling”
“Pattern Discovery”

DATA-DRIVEN COMMUNICATION
DATA-DRIVEN DISCOVERY

ﬁnding ﬁgure/videoAuthoringExploration Presentation
DATA-DRIVEN COMMUNICATION
ﬁnding ﬁgure/videoAuthoringExploration Presentation
Current Model
DATA-DRIVEN DISCOVERY

DATA-DRIVEN COMMUNICATION
ﬁnding ﬁgure/videoAuthoring Presentation
What we show.
DATA-DRIVEN DISCOVERY

DATA-DRIVEN COMMUNICATION
ﬁnding ﬁgure/videoAuthoring Presentation
What we tell.
DATA-DRIVEN DISCOVERY

DATA-DRIVEN COMMUNICATION
ﬁnding ﬁgure/videoAuthoring Presentation
What we did.
DATA-DRIVEN DISCOVERY

DATA-DRIVEN COMMUNICATION
track provenance
annotate observations
make sense of observations
tell the story
DATA-DRIVEN DISCOVERY

DATA-DRIVEN COMMUNICATION
Capture
Label
Understand
Explain
track provenance
annotate observations
make sense of observations
tell the story
C
L
U
E
DATA-DRIVEN DISCOVERY

CLUE
vistories
Authoring
Exploration Presentation
CLUE Model
DATA-DRIVEN COMMUNICATION
DATA-DRIVEN DISCOVERY

VISTORY = visualization + story + history
Do collaborative data analysis.
Use during peer-review.
Publish with a paper.
Embed in a presentation.

DATA-DRIVEN DISCOVERY
DATA-DRIVEN COMMUNICATION

DATA-DRIVEN DISCOVERY
DATA-DRIVEN COMMUNICATION
http://vistories.org

DATA-DRIVEN DISCOVERY
DATA-DRIVEN COMMUNICATION
http://vistories.org
SAMUEL GRATZL
ALEXANDER LEX
MARC STREIT

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Embark on a captivating exploration of the transformative power of biotechnology on economic prosperity and societal advancement in this illuminating presentation. Uncover the multifaceted impacts of biotech breakthroughs across diverse sectors, from bolstering agricultural yields to revolutionizing personalized medicine in healthcare. Delve deep into case studies and real-world examples illustrating how biotechnological innovations serve as catalysts for economic growth and sustainable development. This presentation offers a comprehensive analysis of the strategic imperatives driving the integration of biotechnology into global economies. Discover the pivotal role of public-private partnerships, regulatory frameworks, and investment incentives in fostering an ecosystem conducive to biotech innovation and entrepreneurship. Gain invaluable insights into emerging trends and market dynamics shaping the biotech landscape, from CRISPR gene editing to synthetic biology and beyond. Moreover, explore the ethical, social, and environmental implications inherent in the widespread adoption of biotechnological solutions. Learn how stakeholders navigate complex challenges such as intellectual property rights, biosecurity concerns, and equitable access to biotech advancements while maximizing their positive impact on communities worldwide. Whether you're a seasoned industry professional, an academic researcher, or a policymaker, this presentation promises to deepen your understanding of the pivotal role biotechnology plays in shaping our economic future. Join us on this enlightening journey at the intersection of science, economics, and innovation, and unlock the boundless potential of biotechnology to uplift societies and enhance quality of life on a global scale.

THE ROLE OF BIOTECHNOLOGY IN THE ECONOMIC UPLIFT.pptx

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Understanding Partial Differential Equations: Types and Solution Methods

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Data Visualization in Biomedical Sciences: More than Meets the Eye

1. Data Visualization in Biomedical Sciences: More than Meets the Eye Nils Gehlenborg, PhD Department of Biomedical Informatics Harvard Medical School http://gehlenborglab.org @ngehlenborghttp://gehlenborglab.org

2. datavisyn Co-Founder

3. Thank you!

4. Data Visualization in Biomedical Sciences: More than Meets the Eye Nils Gehlenborg, PhD Department of Biomedical Informatics Harvard Medical School http://gehlenborglab.org @ngehlenborghttp://gehlenborglab.org

5. http://gehlenborglab.org

6. http://gehlenborglab.org Research Data Clinical Data

7. http://gehlenborglab.org Research Data Clinical Data Casual Users Expert Users

8. http://gehlenborglab.org Clinical Data Casual Users Expert Users Research Data

9. http://higlass.io http://hipiler.higlass.io http://caleydo.org/tools/stratomex http://halyos.gehlenborglab.org https://vcg.github.io/upset OncoThreads http://gehlenborglab.org

10. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation hypothesis generation

11. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation hypothesis generation EXPLANATION “Storytelling”

12. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation hypothesis generation EXPLORATION EXPLANATION “Storytelling” “Pattern Discovery”

13. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation HYPOTHESIS hypothesis generation EXPLORATION EXPLANATION “Storytelling” “Pattern Discovery” HYPOTHESIS-DRIVEN DISCOVERY

14. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation DATA hypothesis generation EXPLORATION EXPLANATION “Storytelling” “Pattern Discovery” DATA-DRIVEN DISCOVERY

15. Insight EXPLORATION EXPLANATION

16. Adobe IllustratorCytoscape Insight EXPLORATION EXPLANATION

17. Insight EXPLORATION EXPLANATION

18. Researchers Researchers Decision Makers Policy Makers Citizens … Insight EXPLORATION EXPLANATION Decision Makers Policy Makers Citizens …

19. Researchers Researchers Decision Makers Policy Makers Citizens … Insight EXPLORATION EXPLANATION Decision Makers Policy Makers Citizens …

20. Researchers Researchers Decision Makers Policy Makers Citizens … Insight EXPLORATION EXPLANATION Decision Makers Policy Makers Citizens …

21. WHY SHOULD WE CARE?

22. Nature asked 1,576 researchers if there is a reproducibility crisis in science. M Baker, Nature 533, 452-454, 2016

23. 0% 100% No crisis (3%) Don’t know (7%) Slight crisis (38%) M Baker, Nature 533, 452-454, 2016 Signiﬁcant crisis (52%) Nature asked 1,576 researchers if there is a reproducibility crisis in science.

24. M Baker, Nature 533, 452-454, 2016

25. M Baker, Nature 533, 452-454, 2016

26. M Baker, Nature 533, 452-454, 2016

27. Intentional? Inability to capture everything? Inability to communicate everything? M Baker, Nature 533, 452-454, 2016

28. Intentional? Inability to capture everything? Inability to communicate everything? SOCIAL ISSUE TECHNICAL ISSUES M Baker, Nature 533, 452-454, 2016

29. HOW IS THIS RELATED TO DATA VISUALIZATION?

30. Tumor Subtypes PROBLEM 1 Visualize overlap of patient sets across two or more stratifications. PROBLEM 2 Visualize characteristics of patient sets within a stratification of interest.

31. M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, P Park, N Gehlenborg , Nature Methods (2014) StratomeX

32.

33. Tumor Subtypes PROBLEM 1 Visualize overlap of patient sets across two or more stratifications. PROBLEM 2 Visualize characteristics of patient sets within a stratification of interest. PROBLEM 3 Identify relevant stratifications, pathways, and clinical variables.

34. Is there a mutation that overlaps with this mRNA cluster? Is there a CNV that affects survival? Is there a pathway that is enriched in this cluster? Is there a mutually exclusive mutation? Query Stratifications Clinical Params Pathways Guided Exploration M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, P Park, N Gehlenborg , Nature Methods (2014)

35. Query Rank Visualize Stratifications Clinical Params Pathways Guided Exploration M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, P Park, N Gehlenborg , Nature Methods (2014)

36.

37. ?

38.

39.

40. AND NOW WHAT?

41. DATA-DRIVEN DISCOVERY REPORT experiment DATA INSIGHT HYPOTHESIS interpretation DATA hypothesis generation EXPLORATION EXPLANATION “Storytelling” “Pattern Discovery”

42. DATA-DRIVEN DISCOVERY REPORT experiment DATA INSIGHT HYPOTHESIS interpretation DATA hypothesis generation EXPLORATION EXPLANATION “Storytelling” “Pattern Discovery”

43. DATA-DRIVEN COMMUNICATION DATA-DRIVEN DISCOVERY

44. finding figure/videoAuthoringExploration Presentation DATA-DRIVEN COMMUNICATION finding figure/videoAuthoringExploration Presentation Current Model DATA-DRIVEN DISCOVERY

45. DATA-DRIVEN COMMUNICATION ﬁnding ﬁgure/videoAuthoring Presentation What we show. DATA-DRIVEN DISCOVERY

46. DATA-DRIVEN COMMUNICATION ﬁnding ﬁgure/videoAuthoring Presentation What we tell. DATA-DRIVEN DISCOVERY

47. DATA-DRIVEN COMMUNICATION ﬁnding ﬁgure/videoAuthoring Presentation What we did. DATA-DRIVEN DISCOVERY

48. DATA-DRIVEN COMMUNICATION DATA-DRIVEN DISCOVERY

49. DATA-DRIVEN COMMUNICATION track provenance annotate observations make sense of observations tell the story DATA-DRIVEN DISCOVERY

50. DATA-DRIVEN COMMUNICATION Capture Label Understand Explain track provenance annotate observations make sense of observations tell the story C L U E DATA-DRIVEN DISCOVERY

51. CLUE vistories Authoring Exploration Presentation CLUE Model DATA-DRIVEN COMMUNICATION DATA-DRIVEN DISCOVERY

52. Exploration Authoring Presentation

53. Exploration Authoring Presentation

54. Exploration Authoring Presentation

55. Exploration Authoring Presentation

56. Exploration Authoring Presentation

57. Exploration Authoring Presentation

58. Exploration Authoring Presentation

59. Exploration Authoring Presentation

60. VISTORY = visualization + story + history Do collaborative data analysis. Use during peer-review. Publish with a paper. Embed in a presentation.

61. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION

62. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION http://vistories.org

63. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION http://vistories.org SAMUEL GRATZL ALEXANDER LEX MARC STREIT

64. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION http://vistories.org

65. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation DATA hypothesis generation EXPLORATION EXPLANATION “Storytelling” “Pattern Discovery” DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION

Data Visualization in Biomedical Sciences: More than Meets the Eye

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