Data Visualization to Enhance our Understanding of the Cancer Genome
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This document discusses using data visualization techniques to enhance understanding of cancer genomics data from The Cancer Genome Atlas (TCGA). It describes how visualization can support data-driven discovery, including hypothesis generation, interpretation, and communication of insights. Key visualization approaches outlined include clustering analyses, integrated heatmaps, genome browsers, and StratomeX for exploring stratifications and patient subsets. The document advocates an approach called CLUE (Capture, Label, Understand, Explain) and visual storytelling with Vistories to support data-driven communication and discovery.
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Data Visualization to Enhance our Understanding of the Cancer Genome
- 1. Data Visualization to Enhance our Understanding of the Cancer Genome HARVARD MEDICAL SCHOOL DEPARTMENT OF BIOMEDICAL INFORMATICS NILS GEHLENBORG @nils_gehlenborg http://gehlenborglab.org
- 2. SAMUEL GRATZL JOHANNES KEPLER UNIVERSITY LINZ ALEXANDER LEX UNIVERSITY OF UTAH MARC STREIT JOHANNES KEPLER UNIVERSITY LINZ
- 9. experiment DATA INSIGHT HYPOTHESIS interpretation DATA hypothesis generation EXPLORATION DATA-DRIVEN DISCOVERY “Pattern Discovery”
- 11. The Cancer Genome Atlas 10,000+ genomes + clinical data + molecular data
- 13. CANCER SUBTYPES
- 16. mRNA expression microRNA expression DNA methylation protein expression copy number variants mutation calls clinical parameters
- 19. C4C3C2C1 mRNA expression copy number variants - clustering - gene X
- 20. C4C3C2C1 mRNA expression copy number variants DEL NORMAL AMP - clustering - gene X
- 21. DEL NORMAL AMP C4C3C2C1 mRNA expression copy number variants mutation calls - clustering - gene X - gene Y
- 22. DEL NORMAL AMP C4C3C2C1 mRNA expression copy number variants mutation calls WILDTYPEMUT - clustering - gene X - gene Y
- 23. DEL NORMAL AMP C4C3C2C1 mRNA expression copy number variants mutation calls WILDTYPEMUT - clustering - gene X - gene Y
- 24. L NORMAL AMP C4C3C2C1 mRNA expression copy number variants mutation calls WILDTYPEMUT - clustering - gene X - gene Y
- 25. ALGORITHMIC APPROACHES VISUALIZATION APPROACHES unsupervised clustering of multivariate data integrated clustering across data types (e.g., Mo et al., PNAS, 2013) correlation testing integrative heatmaps network-based stratification (Hofree et al., Nat Methods, 2014)
- 26. Verhaak et al., Cancer Cell, 2010 Matrix Visualization: Publication Figure
- 27. ALGORITHMIC APPROACHES VISUALIZATION APPROACHES unsupervised clustering of multivariate data integrated clustering across data types (e.g., Mo et al., PNAS, 2013) correlation testing integrative heatmaps network-based stratification (Hofree et al., Nat Methods, 2014) genome browsers (UCSC Cancer Genomics Browser, IGV)
- 28. Robinson et al., Nat Biotech, 2011 Genome-Centric Visualization: IGV
- 29. PROBLEM 1 Visualize overlap of patient sets across two or more stratifications. PROBLEM 2 Visualize characteristics of patient sets within a stratification of interest.
- 30. 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
- 31. 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.
- 32. 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
- 33. 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
- 44. AND NOW WHAT?
- 45. DATA-DRIVEN DISCOVERY experiment DATA INSIGHT HYPOTHESIS interpretation DATA hypothesis generation EXPLORATION “Pattern Discovery”
- 48. finding figure/videoAuthoringExploration Presentation DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION finding figure/videoAuthoringExploration Presentation Current Model
- 49. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION figure/videoAuthoring Presentat What we show.
- 50. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION figure/videoAuthoring Presentat What we tell.
- 51. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION figure/videoAuthoring Presentat What we did.
- 53. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION track provenance annotate observations make sense of observations tell the story
- 54. 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
- 55. CLUE vistories Authoring Exploration Presentation DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION CLUE Model
- 64. VISTORY = visual story + history Do collaborative data analysis. Use during peer-review. Publish with a paper. Embed in a presentation.
- 67. DATA-DRIVEN DISCOVERY DATA-DRIVEN COMMUNICATION http://vistories.org Demos and prototypes built with
- 68. We are hiring postdocs & developers! 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: • genomic structural variants • dynamics of the 3D genome • cancer subtypes in patient cohorts • exploration tools for data repositories • provenance graphs