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Guided visual exploration
of patient stratifications in
cancer genomics
Nils Gehlenborg
Harvard Medical School

Center for ...
Machine
Human
Machine
Human
Machine
Human
Data
INTERPRETATION
GENERATIONCOMPUTATION
Machine
Human
Data
“In every chain of reasoning, the evidence of the last conclusion can
be no greater than that of the weakest link of the c...
INTERPRETATION
GENERATIONCOMPUTATION
Machine
Human
Data
Hypotheses
Discoveries
Knowledge
Cognition
x  y1 y2
1.00 0.96 0.76
2.00 0.76 -0.14
3.00 -0.14 -0.91
4.00 -0.91 -0.84
5.00 -0.84 0.00
6.00 0.00 0.84
7.00 0.84 0.91
8....
-1
-0.5
0
0.5
1
1 2 3 4 5 6 7 8 9 10 11
INTERPRETATION
GENERATIONCOMPUTATION
Machine
Human
Hypotheses
Discoveries
Knowledge
Data
Cognition
INTERPRETATION
GENERATIONCOMPUTATION
Machine
Human
Hypotheses
Discoveries
Knowledge
Data|
Cognition
?
TCGAThe Cancer Genome Atlas
mRNA expression
microRNA expression
DNA methylation
protein expression
copy number variants
mutation calls
clinical parame...
Stratome
Anthony92931 / Wikimedia Commons
StratomeX
A Lex, M Streit, H-J Schulz, C Partl, D Schmalstieg, PJ Park, N Gehlenborg, “StratomeX: Visual Anal-
ysis of Lar...
Comparing Patient Sets
across StratificationsPROBLEM 1
Comparing Patient Sets
within StratificationsPROBLEM 2
Finding Relevant
Stratifications and PathwaysPROBLEM 3
?
Knowledge-driven Exploration
Data-driven Exploration
Query
Rank
Visualize
Stratifications
Clinical Params
Pathways
Guided Exploration
LineUp
S Gratzl, A Lex, N Gehlenborg, H Pfister and M Streit, “LineUp: Visual Analysis of Multi-Attribute
Rankings“, IEEE T...
Query
Rank
Visualize
Stratifications
Clinical Params
Pathways
Guided Exploration
INTERPRETATION
GENERATIONCOMPUTATION
Machine
Human
Hypotheses
Discoveries
Knowledge
Data|
Cognition
www.caleydo.org
www.caleydo.org
www.caleydo.org
Domino
S Gratzl, N Gehlenborg, A Lex, H Pfister and M Streit, “Domino: Extracting, Comparing, and
Manipulating Subsets acro...
MD Anderson Cancer Center
University of Rostock
Peter J Park
Michael S Noble, David Heiman, Firehose Team, Gad Getz
Terren...
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
Guided visual exploration of patient stratifications in cancer genomics
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Guided visual exploration of patient stratifications in cancer genomics

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Talk presented at the "Beyond the Genome 2014: Cancer Genomics" conference (10 October 2014)

http://www.beyond-the-genome.com/2014/

Cancer is a heterogeneous disease, and molecular profiling of tumors from large cohorts has enabled characterization of new tumor subtypes. This is a prerequisite for improving personalized treatment and ultimately better patient outcomes. Potential tumor subtypes can be identified with methods such as unsupervised clustering or network-based stratification, which assign patients to sets based on high-dimensional molecular profiles. Detailed characterization of identified sets and their interpretation, however, remain a time-consuming exploratory process.

To address these challenges, we have developed StratomeX (http://stratomex.caleydo.org), an interactive visualization tool that complements algorithmic approaches. StratomeX also integrates a computational framework for query-based guided exploration directly into the visualization, enabling discovery of novel relationships between patient sets and efficient generation and refinement of hypotheses about tumor subtypes. StratomeX enables analysts to efficiently compare multiple patient stratifications, to correlate patient sets with clinical information or genomic alterations, and to view the differences between molecular profiles across patient sets.

Published in: Science
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Guided visual exploration of patient stratifications in cancer genomics

  1. 1. Guided visual exploration of patient stratifications in cancer genomics Nils Gehlenborg Harvard Medical School
 Center for Biomedical Informatics nils_gehlenborg
  2. 2. Machine Human
  3. 3. Machine Human
  4. 4. Machine Human Data
  5. 5. INTERPRETATION GENERATIONCOMPUTATION Machine Human Data
  6. 6. “In every chain of reasoning, the evidence of the last conclusion can be no greater than that of the weakest link of the chain, whatever may be the strength of the rest.” - Thomas Reid, Essays on the Intellectual Powers of Man (1786)
  7. 7. INTERPRETATION GENERATIONCOMPUTATION Machine Human Data Hypotheses Discoveries Knowledge Cognition
  8. 8. x  y1 y2 1.00 0.96 0.76 2.00 0.76 -0.14 3.00 -0.14 -0.91 4.00 -0.91 -0.84 5.00 -0.84 0.00 6.00 0.00 0.84 7.00 0.84 0.91 8.00 0.91 0.14 9.00 0.14 -0.76 10.00 -0.76 -0.96 11.00 -0.96 -0.28
  9. 9. -1 -0.5 0 0.5 1 1 2 3 4 5 6 7 8 9 10 11
  10. 10. INTERPRETATION GENERATIONCOMPUTATION Machine Human Hypotheses Discoveries Knowledge Data Cognition
  11. 11. INTERPRETATION GENERATIONCOMPUTATION Machine Human Hypotheses Discoveries Knowledge Data| Cognition
  12. 12. ?
  13. 13. TCGAThe Cancer Genome Atlas
  14. 14. mRNA expression microRNA expression DNA methylation protein expression copy number variants mutation calls clinical parameters
  15. 15. Stratome
  16. 16. Anthony92931 / Wikimedia Commons
  17. 17. StratomeX A Lex, M Streit, H-J Schulz, C Partl, D Schmalstieg, PJ Park, N Gehlenborg, “StratomeX: Visual Anal- ysis of Large-Scale Heterogeneous Genomics Data for Cancer Subtype Characterization“, Computer Graphics Forum 31:1175-1184 (2012) M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, PJ Park, N Gehlenborg, “Guided Visual Exploration of Genomic Stratifications in Cancer“, Nature Methods 11:884-885 (2014)
  18. 18. Comparing Patient Sets across StratificationsPROBLEM 1
  19. 19. Comparing Patient Sets within StratificationsPROBLEM 2
  20. 20. Finding Relevant Stratifications and PathwaysPROBLEM 3
  21. 21. ?
  22. 22. Knowledge-driven Exploration Data-driven Exploration
  23. 23. Query Rank Visualize Stratifications Clinical Params Pathways Guided Exploration
  24. 24. LineUp S Gratzl, A Lex, N Gehlenborg, H Pfister and M Streit, “LineUp: Visual Analysis of Multi-Attribute Rankings“, IEEE Transactions on Visualization and Computer Graphics 19:2277-2286 (2013)
  25. 25. Query Rank Visualize Stratifications Clinical Params Pathways Guided Exploration
  26. 26. INTERPRETATION GENERATIONCOMPUTATION Machine Human Hypotheses Discoveries Knowledge Data| Cognition
  27. 27. www.caleydo.org
  28. 28. www.caleydo.org
  29. 29. www.caleydo.org
  30. 30. Domino S Gratzl, N Gehlenborg, A Lex, H Pfister and M Streit, “Domino: Extracting, Comparing, and Manipulating Subsets across Multiple Tabular Datasets“, IEEE Transactions on Visualization and Computer Graphics (2014)
  31. 31. MD Anderson Cancer Center University of Rostock Peter J Park Michael S Noble, David Heiman, Firehose Team, Gad Getz Terrence Wu, Ian Watson, Lynda Chin Harvard Medical School Broad Institute of MIT & Harvard Christian Partl, Dieter SchmalstiegGraz University of Technology Johannes Kepler University Linz Samuel Gratzl, Marc Streit Hans-Jörg Schulz Acknowledgements Harvard SEAS Alexander Lex, Hanspeter Pfister Funding NIH/NHGRI K99 HG007583 Pathway to Independence

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