Patients, Genomes, Time: Visualizing Disease Cohorts
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Interpreting data from cohort studies where clinical and molecular data across hundreds to thousands of patient samples need to be integrated, potentially spanning multiple time points, is challenging. In this presentation, I will discuss how data visualization can be used to drive or support this process, using tools that are applying the concept of “divide and conquer” to visual exploration. I will be presenting our early work on StratomeX and illustrate how this approach led to techniques such as Domino and LineUp, and will also introduce OncoThreads and Lineage, tools that we designed for visualization of cohorts with temporal and genealogical information, respectively.
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Patients, Genomes, Time: Visualizing Disease Cohorts
- 1. Patients, Genomes, Time: Visualizing Disease Cohorts Nils Gehlenborg, PhD Department of Biomedical Informatics Harvard Medical School http://gehlenborglab.org | nils@hms.harvard.edu | @ngehlenborg
- 3. Patients, Genomes, Time: Visualizing Disease Cohorts Nils Gehlenborg, PhD Department of Biomedical Informatics Harvard Medical School http://gehlenborglab.org | nils@hms.harvard.edu | @ngehlenborg Disease Cohorts
- 5. 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.
- 6. 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 Funded by NIH TCGA
- 7. The Cancer Genome Atlas 10,000+ patients 20+ tumor types
- 8. 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
- 9. C4C3C2C1 LONGER TYPICAL SHORTER WILDTYPEMUT patient survival time mutation status of gene Y mRNA expression clustering Tumor Subtypes
- 10. C4C3C2C1 WILDTYPEMUT mRNA expression clustering patient survival time mutation status of gene Y Tumor Subtypes LONGER TYPICAL SHORTER
- 11. C4C3C2C1 WILDTYPEMUT mRNA expression clustering patient survival time mutation status of gene Y Tumor Subtypes LONGER TYPICAL SHORTER
- 12. C4C3C2C1 WILDTYPEMUT mRNA expression clustering patient survival time mutation status of gene Y Tumor Subtypes LONGER TYPICAL SHORTER
- 13. C4C3C2C1 WILDTYPEMUT patient survival time mutation status of gene Y mRNA expression clustering Tumor Subtypes LONGER TYPICAL SHORTER
- 14. 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.
- 15. M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, P Park, N Gehlenborg , Nature Methods (2014) StratomeX: Divide & Conquer
- 19. 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.
- 20. 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 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)
- 22. 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 Funded by NIH BD2K
- 23. 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)
- 27. 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 Funded by NIH BD2K
- 28. 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
- 29. 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
- 30. 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
- 31. 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 Funded by NIH/NHGRI
- 32. Motivation
- 33. 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
- 34. TCGA Example S Gratzl, N Gehlenborg, A Lex, H Pfister, M Streit, TVCG (2014)
- 35. Blocks: Partitioned Numerical Matrix S Gratzl, N Gehlenborg, A Lex, H Pfister, M Streit, TVCG (2014) Types
- 36. Blocks: Partitioned Numerical Matrix S Gratzl, N Gehlenborg, A Lex, H Pfister, M Streit, TVCG (2014) Representations
- 37. S Gratzl, N Gehlenborg, A Lex, H Pfister, M Streit, TVCG (2014) Blocks: Relationships
- 38. S Gratzl, N Gehlenborg, A Lex, H Pfister, M Streit, TVCG (2015) Supported Techniques
- 39. 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 Funded by NIH/NHGRI
- 40. 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
- 42. Talk about OncoThreads tomorrow at 5:40 pm in the BioVis COSI or visit us at poster B763!
- 43. 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 Funded by NIH BD2K
- 45. Aggregation affected with family context affected without family context
- 48. Conclusion
- 49. 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
- 50. Next Steps Better integration of specialized visualizations with support for faceting and aggregation Scale to 100Ks or Ms of individuals (UK Biobank, All of Us, etc.) Integration with data management systems (e.g. i2b2 TranSMART, 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)