Talk presented at the EMBL-EBI Industry Programme workshop on "Innovations in Visualization for Drug Discovery" in June 2018.

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

3. Patient Cohorts

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

8. mRNA expression

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