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 for Biomedical 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 1,576 researchers 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.
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 Provenance Information
ED Ragan et al., IEEE Transactions on Visualization and Computer Graphics 22, 31 – 40, 2015
32. DEL NORMAL AMP
C4C3C2C1
mRNA expression
copy number variants
mutation calls
WILDTYPEMUT
- clustering
- gene X
- gene Y
33. DEL NORMAL AMP
C4C3C2C1
mRNA expression
copy number variants
mutation calls
WILDTYPEMUT
- clustering
- gene X
- gene Y
34. L NORMAL AMP
C4C3C2C1
mRNA expression
copy number variants
mutation calls
WILDTYPEMUT
- clustering
- gene X
- gene Y
35. PROBLEM 1
Visualize overlap of patient sets across two or more stratifications.
PROBLEM 2
Visualize characteristics of patient sets within a stratification of interest.
36. A Lex, M Streit, 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 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.
38. 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, PJ Park, N Gehlenborg, Nat Methods, 2014
66. SAMUEL GRATZL
JOHANNES KEPLER UNIVERSITY LINZ
ALEXANDER LEX
UNIVERSITY OF UTAH
MARC STREIT
JOHANNES KEPLER UNIVERSITY LINZ
HOLGER STITZ
JOHANNES KEPLER UNIVERSITY LINZ
67. My lab is hiring 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