Visualizing Patient Cohorts: Integrating Data Types, Relationships, and Time
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The document discusses advanced visualization techniques for analyzing patient cohorts in cancer genomics, emphasizing the integration of diverse data types such as omics data and clinical parameters. It highlights tools like Stratomex and Domino for exploring patient stratifications and managing tabular datasets, with a focus on enhancing data interpretation and guided exploration. The conclusion points to the importance of collaboration and adaptation in visualization methods for real-world applications and long-term insights in cancer research.
Visualizing Patient Cohorts: Integrating Data Types, Relationships, and Time
- 1. Visualizing Patient Cohorts Integrating Data Types, Relationships, and Time Nils Gehlenborg, PhD Department of Biomedical Informatics Harvard Medical School http://gehlenborglab.org | nils@hms.harvard.edu | @ngehlenborg
- 4. Typical 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.
- 5. 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
- 6. The Cancer Genome Atlas 10,000+ patients 20+ tumor types
- 7. 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 mRNA expression clustering Tumor Subtypes
- 10. C4C3C2C1 DEL NORMAL AMP copy number of gene X mRNA expression clustering Tumor Subtypes
- 11. C4C3C2C1 DEL NORMAL AMP WILDTYPEMUT copy number of gene X mutation status of gene Y mRNA expression clustering Tumor Subtypes
- 12. C4C3C2C1 DEL NORMAL AMP WILDTYPEMUT mRNA expression clustering copy number of gene X mutation status of gene Y Tumor Subtypes
- 13. C4C3C2C1 DEL NORMAL AMP WILDTYPEMUT mRNA expression clustering copy number of gene X mutation status of gene Y Tumor Subtypes
- 14. C4C3C2C1 DEL NORMAL AMP WILDTYPEMUT mRNA expression clustering copy number of gene X mutation status of gene Y Tumor Subtypes
- 15. C4C3C2C1 DEL NORMAL AMP WILDTYPEMUT copy number of gene X mutation status of gene Y mRNA expression clustering Tumor Subtypes
- 16. 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.
- 17. M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, P Park, N Gehlenborg , Nature Methods (2014) StratomeX
- 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 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)
- 21. 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. Individual sets with large overlap: Jaccard Index Overall similarity of stratifications: Adjusted Rand Index Survival: Log Rank Score (one vs rest) Queries to retrieve stratifications Gene Set Enrichtment Score: original GSEA or Parametric Assignment of Gene Set Enrichment (PAGE) (one vs rest) Queries to retrieve pathways L Hubert & P Arabie, Journal of Classification (1985) A Subramanian et al., PNAS (2005) S-Y Kim & DJ Volsky, BMC Bioinformatics (2005) Guided Exploration
- 23. Query Rank Visualize Stratifications Clinical Params Pathways M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, P Park, N Gehlenborg , Nature Methods (2014) Guided Exploration
- 36. 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)
- 43. 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(12), 2023-2032, 2014 Samuel Gratzl datavisyn Marc Streit JKU Linz Alexander Lex University of Utah Domino Extracting, Comparing, and Manipulating Subsets across Tabular Datasets
- 44. 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
- 45. TCGA Example S Gratzl, N Gehlenborg, A Lex, H Pfister, M Streit, TVCG (2014)
- 46. Blocks: Partitioned Numerical Matrix S Gratzl, N Gehlenborg, A Lex, H Pfister, M Streit, TVCG (2014) Types
- 47. Blocks: Partitioned Numerical Matrix S Gratzl, N Gehlenborg, A Lex, H Pfister, M Streit, TVCG (2014) Representations
- 48. S Gratzl, N Gehlenborg, A Lex, H Pfister, M Streit, TVCG (2014) Blocks: Relationships
- 49. S Gratzl, N Gehlenborg, A Lex, H Pfister, M Streit, TVCG (2015) Supported Techniques
- 52. 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
- 53. Motivation 1. Cohorts of patients with longitudinal sample information 2. Information about what happened between samples critical for interpretation 3. Application to longitudinal cancer cohorts or clinical trials
- 55. Conclusion
- 56. 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
- 57. 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)
- 58. Broadening Impact Consider role of academic visualization research in real-world settings - novel visualization techniques are informing future work (cf. Domino) - can fill niche not addressed by or not viable for commercial products Collaboration with industry beginning at day 1 are ideal (cf. OncoThreads) - generally true for any visualization project and any project partner! Better education about strengths and weaknesses of visualization to avoid disappointment and frustration by investing in the right places