Cancer Genomics Visualization across Scales: Nucleotides to Cohorts
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AMP 2018 Talk in "Show Me the Data" workshop.
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Cancer Genomics Visualization across Scales: Nucleotides to Cohorts
- 1. Cancer Genomics Visualization across Scales: Nucleotides to Cohorts Nils Gehlenborg, PhD Department of Biomedical Informatics Harvard Medical School http://gehlenborglab.org | nils@hms.harvard.edu | @ngehlenborg
- 2. Co-Founder
- 3. Nils Gehlenborg, PhD Department of Biomedical Informatics Harvard Medical School http://gehlenborglab.org | nils@hms.harvard.edu | @ngehlenborg Cancer Genomics Visualization across Scales: Nucleotides to CohortsCohorts
- 4. Cohorts
- 7. Characteristics Dozens to thousands of patients
- 8. Characteristics Dozens to thousands of patients
- 9. Characteristics Dozens to thousands of patients One or more samples per patient: tumor & normal tissue, primary tumor & metastatic tumor(s), multiple time points, etc.
- 10. Characteristics Dozens to thousands of patients One or more samples per patient: tumor & normal tissue, primary tumor & metastatic tumor(s), multiple time points, etc.
- 11. Characteristics Dozens to thousands of patients One or more samples per patient: tumor & normal tissue, primary tumor & metastatic tumor(s), multiple time points, etc. Many attributes per sample: omics data, clinical measurements, outcomes, etc.
- 12. StratomeX Discovering Subtypes in Tumor Cohorts
- 13. StratomeX Discovering Subtypes in Tumor Cohorts Samuel Gratzl datavisyn Marc Streit JKU Linz Alexander Lex University of Utah
- 14. StratomeX Discovering Subtypes in Tumor Cohorts Marc Streit, Alexander Lex, Samuel Gratzl, Christian Partl, Dieter Schmalstieg, Hanspeter Pfister, Peter Park, Nils Gehlenborg Guided Visual Exploration of Genomic Stratifications in Cancer Nature Methods, 11, 884–885, 2014 Samuel Gratzl datavisyn Marc Streit JKU Linz Alexander Lex University of Utah
- 17. The Cancer Genome Atlas 10,000+ patients 20+ tumor types
- 18. The Cancer Genome Atlas 10,000+ patients 20+ tumor types
- 19. mRNA expression The Cancer Genome Atlas 10,000+ patients 20+ tumor types
- 20. microRNA expression mRNA expression The Cancer Genome Atlas 10,000+ patients 20+ tumor types
- 21. microRNA expression protein expression mRNA expression The Cancer Genome Atlas 10,000+ patients 20+ tumor types
- 22. microRNA expression protein expression mutation calls mRNA expression The Cancer Genome Atlas 10,000+ patients 20+ tumor types
- 23. microRNA expression protein expression copy number variants mutation calls mRNA expression The Cancer Genome Atlas 10,000+ patients 20+ tumor types
- 24. microRNA expression DNA methylation protein expression copy number variants mutation calls mRNA expression The Cancer Genome Atlas 10,000+ patients 20+ tumor types
- 25. microRNA expression DNA methylation protein expression copy number variants mutation calls clinical parameters mRNA expression The Cancer Genome Atlas 10,000+ patients 20+ tumor types
- 26. mRNA expression
- 27. C4C3C2C1 mRNA expression clustering Tumor Subtypes
- 28. C4C3C2C1 LONGER TYPICAL SHORTER patient survival time mRNA expression clustering Tumor Subtypes
- 29. C4C3C2C1 LONGER TYPICAL SHORTER WILDTYPEMUT patient survival time mutation status of gene Y mRNA expression clustering Tumor Subtypes
- 30. C4C3C2C1 WILDTYPEMUT mRNA expression clustering patient survival time mutation status of gene Y Tumor Subtypes LONGER TYPICAL SHORTER
- 31. C4C3C2C1 WILDTYPEMUT mRNA expression clustering patient survival time mutation status of gene Y Tumor Subtypes LONGER TYPICAL SHORTER
- 32. C4C3C2C1 WILDTYPEMUT mRNA expression clustering patient survival time mutation status of gene Y Tumor Subtypes LONGER TYPICAL SHORTER
- 33. C4C3C2C1 WILDTYPEMUT patient survival time mutation status of gene Y mRNA expression clustering Tumor Subtypes LONGER TYPICAL SHORTER
- 34. Tumor Subtypes
- 35. Tumor Subtypes PROBLEM 1 Visualize overlap of patient sets across two or more stratifications.
- 36. 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.
- 37. M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, P Park, N Gehlenborg , Nature Methods (2014) StratomeX: Divide & Conquer
- 42. 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.
- 43. 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.
- 44. 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)
- 45. 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)
- 46. 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)
- 47. Is there a mutation that overlaps with this mRNA cluster? 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)
- 48. Is there a mutation that overlaps with this mRNA 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)
- 49. Is there a mutation that overlaps with this mRNA cluster? Is there a CNV that affects survival? 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)
- 50. 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)
- 51. Query Rank 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)
- 52. 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)
- 54. StratomeX+ Interactive visual exploration and refinement of cluster assignments
- 55. StratomeX+ Interactive visual exploration and refinement of cluster assignments Alexander Lex University of Utah
- 56. StratomeX+ Interactive visual exploration and refinement of cluster assignments Michael Kern, Alexander Lex, Nils Gehlenborg, Christopher R Johnson Interactive visual exploration and refinement of cluster assignments BMC Bioinformatics 18:406 (2017) Alexander Lex University of Utah
- 57. Cluster Refinement M Kern, A Lex, N Gehlenborg, C Johnson, BMC Bioinformatics (2017)
- 58. Cluster Refinement Adjust cluster (i.e. subtype) membership based on within- and between-cluster metrics in context of other data M Kern, A Lex, N Gehlenborg, C Johnson, BMC Bioinformatics (2017)
- 59. Cluster Refinement Adjust cluster (i.e. subtype) membership based on within- and between-cluster metrics in context of other data M Kern, A Lex, N Gehlenborg, C Johnson, BMC Bioinformatics (2017)
- 66. Vistories From Visual Exploration to Storytelling and Back Again
- 67. Vistories From Visual Exploration to Storytelling and Back Again Samuel Gratzl datavisyn Marc Streit JKU Linz Alexander Lex University of Utah
- 68. Vistories From Visual Exploration to Storytelling and Back Again Samuel Gratzl, Alexander Lex, Nils Gehlenborg, Nicola Cosgrove, Marc Streit From Visual Exploration to Storytelling and Back Again Computer Graphics Forum (EuroVis ’16) 35:491 (2016) Samuel Gratzl datavisyn Marc Streit JKU Linz Alexander Lex University of Utah
- 69. Reproducible Visual Exploration finding figure/videoAuthoringExploration Presentation Current Model S Gratzl, A Lex, N Gehlenborg, N Cosgrove, M Streit, Computer Graphics Forum (2016), http://vistories.org
- 70. Reproducible Visual Exploration finding figure/videoAuthoringExploration Presentation Current Model Visualization Tool e.g. Illustrator e.g. PDF Viewer S Gratzl, A Lex, N Gehlenborg, N Cosgrove, M Streit, Computer Graphics Forum (2016), http://vistories.org
- 71. Reproducible Visual Exploration Vistories CLUE vistories Authoring Exploration Presentation Vistories-enabled Visualization Tool S Gratzl, A Lex, N Gehlenborg, N Cosgrove, M Streit, Computer Graphics Forum (2016), http://vistories.org
- 72. Domino Extracting, Comparing, and Manipulating Subsets across Tabular Datasets
- 73. Samuel Gratzl datavisyn Marc Streit JKU Linz Alexander Lex University of Utah Domino Extracting, Comparing, and Manipulating Subsets across Tabular Datasets
- 74. Samuel Gratzl, Nils Gehlenborg, Alexander Lex, Hanspeter Pfister, Marc Streit Domino: Extracting, Comparing, and Manipulating Subsets across Multiple Tabular Datasets IEEE Transactions on Visualization and Computer Graphics (InfoVis '14) 20:2023 (2014) Samuel Gratzl datavisyn Marc Streit JKU Linz Alexander Lex University of Utah Domino Extracting, Comparing, and Manipulating Subsets across Tabular Datasets
- 75. Motivation
- 76. Motivation
- 77. Motivation
- 78. Motivation 1. StratomeX is limited to a rigid columnar layout
- 79. Motivation 1. StratomeX is limited to a rigid columnar layout 2. StratomeX only shows connections on a block level, not for individual samples
- 80. Motivation 1. StratomeX is limited to a rigid columnar layout 2. StratomeX only shows connections on a block level, not for individual samples 3. StratomeX only supports exploration along the sample/patient dimension
- 81. TCGA Example S Gratzl, N Gehlenborg, A Lex, H Pfister, M Streit, TVCG (2014)
- 82. TCGA Example S Gratzl, N Gehlenborg, A Lex, H Pfister, M Streit, TVCG (2014)
- 83. Blocks: Partitioned Numerical Matrix S Gratzl, N Gehlenborg, A Lex, H Pfister, M Streit, TVCG (2014) Types
- 84. Blocks: Partitioned Numerical Matrix S Gratzl, N Gehlenborg, A Lex, H Pfister, M Streit, TVCG (2014) Representations
- 85. S Gratzl, N Gehlenborg, A Lex, H Pfister, M Streit, TVCG (2014) Blocks: Relationships
- 86. S Gratzl, N Gehlenborg, A Lex, H Pfister, M Streit, TVCG (2015) Supported Techniques
- 87. S Gratzl, N Gehlenborg, A Lex, H Pfister, M Streit, TVCG (2015) Supported Techniques
- 89. OncoThreads Incorporating Longitudinal Information Sabrina Nusrat Harvard Theresa Harbig Harvard Ethan Cerami DFCI Tali Mazor DFCI
- 90. OncoThreads Incorporating Longitudinal Information Theresa Harbig, Sabrina Nusrat, Alex Thomson, Hans Bitter, Tali Mazor, Ethan Cerami, Nils Gehlenborg Visualization of Longitudinal Cancer Genomics Data Work in Progress Sabrina Nusrat Harvard Theresa Harbig Harvard Ethan Cerami DFCI Tali Mazor DFCI
- 91. Motivation
- 92. Motivation 1. Cohorts of patients with longitudinal sample information
- 93. Motivation 1. Cohorts of patients with longitudinal sample information 2. Events between sample collection critical for interpretation
- 94. Motivation 1. Cohorts of patients with longitudinal sample information 2. Events between sample collection critical for interpretation 3. Application to longitudinal cancer cohorts or clinical trials
- 95. State of the Art Miller, Christopher A., et al. "Visualizing tumor evolution with the fishplot package for R." BMC genomics 17.1 (2016): 880.
- 96. State of the Art Need for visualizations of entire patient cohorts instead of single patient to explore temporal patterns Miller, Christopher A., et al. "Visualizing tumor evolution with the fishplot package for R." BMC genomics 17.1 (2016): 880.
- 98. Requirements ● Develop a tool for the visualization of temporal cancer genomic data in patient cohorts http://www.cbioportal.org/
- 99. Requirements ● Develop a tool for the visualization of temporal cancer genomic data in patient cohorts ● Integrate multiple different datatypes http://www.cbioportal.org/
- 100. Requirements ● Develop a tool for the visualization of temporal cancer genomic data in patient cohorts ● Integrate multiple different datatypes ● Web-based and compatible with the cBio Portal http://www.cbioportal.org/
- 102. Design Sprint Knapp, Zeratsky, and Kowitz. Sprint: How to solve big problems and test new ideas in just five days (2016)
- 103. Design Sprint
- 104. Design Sprint
- 105. Design Sprint
- 106. Visualizing a patient over time Grade II 39 Grade IV 1226 Time Neoplasm Histologic Grade Mutation Count Neoplasm Histologic Grade Mutation Count Two samples at different timepoints represented by two variables
- 107. Visualizing a patient over time What could explain the change? Did the patient receive a treatment? Grade II 39 Grade IV 1226 Time Neoplasm Histologic Grade Mutation Count Neoplasm Histologic Grade Mutation Count Two samples at different timepoints represented by two variables
- 108. Visualizing a patient over time Grade II 39 Grade IV 1229 Time Neoplasm Histologic Grade Mutation Count Neoplasm Histologic Grade Mutation Count Treatment Treatment Treatment No TMZ TMZ No TMZ
- 109. Visualizing a patient over time Is this a common pattern? Grade II 39 Grade IV 1229 Time Neoplasm Histologic Grade Mutation Count Neoplasm Histologic Grade Mutation Count Treatment Treatment Treatment No TMZ TMZ No TMZ
- 112. Sorting and Grouping
- 119. Domino Gratzl, Samuel, et al. "Domino: Extracting, comparing, and manipulating subsets across multiple tabular datasets." IEEE transactions on visualization and computer graphics 20.12 (2014): 2023-2032.
- 120. Domino Gratzl, Samuel, et al. "Domino: Extracting, comparing, and manipulating subsets across multiple tabular datasets." IEEE transactions on visualization and computer graphics 20.12 (2014): 2023-2032.
- 121. Timeline
- 127. Summary - Temporal Cancer genomic data can be visualized using temporal heatmaps and Sankey diagrams http://oncothreads.gehlenborglab.org
- 128. Summary - Temporal Cancer genomic data can be visualized using temporal heatmaps and Sankey diagrams - Domino inspired some of our design choices http://oncothreads.gehlenborglab.org
- 129. Summary - Temporal Cancer genomic data can be visualized using temporal heatmaps and Sankey diagrams - Domino inspired some of our design choices - Design Sprint Technique helped us to develop a new concept within only five days http://oncothreads.gehlenborglab.org
- 130. Conclusion
- 131. Take Aways
- 132. Take Aways Despite highly heterogeneous data, the “block and ribbon” approaches are able to integrate a wide range of data types
- 133. Take Aways Despite highly heterogeneous data, the “block and ribbon” approaches are able to integrate a wide range of data types Integration of auxiliary visualization types (pathways, Kaplan-Meier plots, box plots, etc.) extend the possibilities
- 134. Take Aways Despite highly heterogeneous data, the “block and ribbon” approaches are able to integrate a wide range of data types Integration of auxiliary visualization types (pathways, Kaplan-Meier plots, box plots, etc.) extend the possibilities Ability to aggregate data is critical to these approaches
- 135. Next Steps
- 136. Next Steps Provide support for guided exploration in OncoThreads
- 137. Next Steps Provide support for guided exploration in OncoThreads Integration with other data management systems (e.g. i2b2 TranSMART, in addition to cBioPortal)
- 138. Next Steps Provide support for guided exploration in OncoThreads Integration with other data management systems (e.g. i2b2 TranSMART, in addition to cBioPortal) - Challenge: generally not designed to support visualization, e.g. aggregation
- 139. Next Steps Provide support for guided exploration in OncoThreads Integration with other data management systems (e.g. i2b2 TranSMART, in addition to cBioPortal) - Challenge: generally not designed to support visualization, e.g. aggregation - Opportunity: easier to deploy visualizations in real-world settings
- 140. Next Steps Provide support for guided exploration in OncoThreads Integration with other data management systems (e.g. i2b2 TranSMART, in addition to cBioPortal) - Challenge: generally not designed to support visualization, e.g. aggregation - Opportunity: easier to deploy visualizations in real-world settings Integration with analytical backends (e.g. Jupyter Notebooks or pipelines)
- 141. Next Steps Provide support for guided exploration in OncoThreads Integration with other data management systems (e.g. i2b2 TranSMART, in addition to cBioPortal) - Challenge: generally not designed to support visualization, e.g. aggregation - Opportunity: easier to deploy visualizations in real-world settings Integration with analytical backends (e.g. Jupyter Notebooks or pipelines) Better integration of specialized visualizations with support for faceting and aggregation
- 142. Nils Gehlenborg, PhD Department of Biomedical Informatics Harvard Medical School http://gehlenborglab.org | nils@hms.harvard.edu | @ngehlenborg Cancer Genomics Visualization across Scales: Nucleotides to CohortsCohorts
- 144. Lineage Incorporating Genealogical Information Carolina Nobre University of Utah Alexander Lex University of Utah
- 145. Lineage Incorporating Genealogical Information Carolina Nobre, Nils Gehlenborg, Hilary Coon, Alexander Lex Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs IEEE Transactions on Visualization and Computer Graphics (2018). To appear. Carolina Nobre University of Utah Alexander Lex University of Utah
- 150. Aggregation
- 152. Aggregation affected with family context affected without family context