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Tracing the Origins of Data and Ideas - Provenance Visualization for Biomedical Discovery
A researcher presented a document on using data visualization and provenance to aid biomedical discovery. Some key points: - There is significant reproducibility crisis in science according to a Nature survey of researchers. - Provenance captures the origins and history of data and insights to improve reproducibility and understanding. - Tools like StratomeX and CLUE were developed to visualize relationships in biomedical data from sources like The Cancer Genome Atlas to guide exploration and discovery. - The presenter advocates for data-driven discovery, communication, and storytelling using visualization histories ("vistories") to better capture the full process and context of insights.
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Tracing the Origins of Data and Ideas - Provenance Visualization for Biomedical Discovery
- 1. Provenance Visualization forBiomedical Discovery HARVARD MEDICAL SCHOOL DEPARTMENT OF BIOMEDICAL INFORMATICS Nils Gehlenborg・http://gehlenborglab.org・@nils_gehlenborg Tracing the Origins of Data and Ideas
- 2. Nature asked 1,576researchers if there is a reproducibility crisis in science. M Baker, Nature 533, 452-454, 2016
- 3. 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.
- 4. M Baker, Nature533, 452-454, 2016
- 5. M Baker, Nature533, 452-454, 2016 Intentional? Inability to capture everything? Inability to communicate everything?
- 6. M Baker, Nature533, 452-454, 2016 Intentional? Inability to capture everything? Inability to communicate everything? SOCIAL ISSUE TECHNICAL ISSUES
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- 8. The term, provenance,has been used in a variety of ways to describe different types of origins and histories. ED Ragan et al., IEEE Transactions on Visualization and Computer Graphics 22, 31 – 40, 2015
- 9. Types of ProvenanceInformation ED Ragan et al., IEEE Transactions on Visualization and Computer Graphics 22, 31 – 40, 2015
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- 23. The Cancer GenomeAtlas 10,000+ genomes + clinical data + molecular data
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- 28. mRNA expression microRNAexpression DNA methylation protein expression copy number variants mutation calls clinical parameters
- 29. mRNA expression microRNAexpression DNA methylation protein expression copy number variants mutation calls clinical parameters
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- 31. C4C3C2C1 mRNA expression copy numbervariants DEL NORMAL AMP - clustering - gene X
- 32. DEL NORMAL AMP C4C3C2C1 mRNAexpression copy number variants mutation calls WILDTYPEMUT - clustering - gene X - gene Y
- 33. DEL NORMAL AMP C4C3C2C1 mRNAexpression copy number variants mutation calls WILDTYPEMUT - clustering - gene X - gene Y
- 34. L NORMAL AMP C4C3C2C1 mRNAexpression copy number variants mutation calls WILDTYPEMUT - clustering - gene X - gene Y
- 35. PROBLEM 1 Visualize overlapof patient sets across two or more stratifications. PROBLEM 2 Visualize characteristics of patient sets within a stratification of interest.
- 36. A Lex, MStreit, H-J Schulz, C Partl, D Schmalstieg, PJ Park, N Gehlenborg, Comput Graph Forum, 2012 M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, PJ Park, N Gehlenborg, Nat Methods, 2014 Divide & Conquer Visualization: StratomeX
- 37. PROBLEM 1 Visualize overlapof 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.
- 38. Is there amutation 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, PJ Park, N Gehlenborg, Nat Methods, 2014
- 39. Query Rank Visualize Stratifications Clinical Params Pathways GUIDED EXPLORATION M Streit,A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, PJ Park, N Gehlenborg, Nat Methods, 2014
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- 44. finding figure/videoAuthoringExploration Presentation DATA-DRIVENDISCOVERY DATA-DRIVEN COMMUNICATION finding figure/videoAuthoringExploration Presentation Current Model
- 45. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION figure/videoAuthoringPresentat What we show.
- 46. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION figure/videoAuthoringPresentat What we tell.
- 47. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION figure/videoAuthoringPresentat What we did.
- 48. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION trackprovenance annotate observations make sense of observations tell the story
- 49. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION Capture Label Understand Explain trackprovenance annotate observations make sense of observations tell the story
- 50. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION Capture Label Understand Explain trackprovenance annotate observations make sense of observations tell the story C L U E
- 51. CLUE vistories Authoring Exploration Presentation DATA-DRIVEN DISCOVERY DATA-DRIVENCOMMUNICATION CLUE Model
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- 60. VISTORY = visualization+ story + history
- 61. VISTORY = visualization+ story + history Do collaborative data analysis. Use during peer-review. Publish with a paper. Embed in a presentation.
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- 64. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION http://vistories.org Demosand prototypes built with
- 66. SAMUEL GRATZL JOHANNES KEPLERUNIVERSITY LINZ ALEXANDER LEX UNIVERSITY OF UTAH MARC STREIT JOHANNES KEPLER UNIVERSITY LINZ HOLGER STITZ JOHANNES KEPLER UNIVERSITY LINZ
- 67. My lab ishiring postdocs! HARVARD MEDICAL SCHOOL DEPARTMENT OF BIOMEDICAL INFORMATICS See http://gehlenborglab.org or http://dbmi.med.harvard.edu for details. Data visualization, analysis, and management for: • exploration tools for data repositories • provenance graphs • genomic structural variants • dynamics of the 3D genome • cancer subtypes in patient cohorts