1. Bridging the Gap Between Pathways and Experimental Data Alexander Lex

2. Experimental Data and Pathways Pathways represent consensus knowledge for a healthy organism or specific disease Cannot account for variation found in real-world data Branches can be (in)activated due to mutation, changed gene expression, modulation due to drug treatment, etc. 2 Alexander Lex | Harvard University

3. Why use Visualization? Efficient communication of information A-3.4 B 2.8 C3.1 D-3 E0.5 F0.3 3 Alexander Lex | Harvard University C B D F A E

4. Experimental Data and Pathways [Lindroos2002] [KEGG] 4 Alexander Lex | Harvard University

5. Visualization Approaches 5 On-Node Mapping Separate Linked Views Small Multiples Layout AdaptionLinearization [Meyer 2010] [Junker 2006] [Lindroos 2002] Alexander Lex | Harvard University Path-Extraction

6. 6 Alexander Lex | Harvard University

7. What to Consider when Visualizing Experimental Data and Pathways Conflicting Goals Preserving topology of pathways Showing lots of experimental data Five Requirements Ideal visualization technique addresses all 7 Alexander Lex | Harvard University

8. R I: Data Scale Large number of experiments Large datasets have more than 500 experiments Multiple groups/conditions 8 Alexander Lex | Harvard University

9. R II: Data Heterogeneity Different types of data, e.g., mRNA expression numerical mutation status categorical copy number variation ordered categorical metabolite concentration numerical Require different visualization techniques 9 Alexander Lex | Harvard University

10. R III: Multi-Mapping Pathways nodes are biomolecules Proteins, nucleic acids, lipids, metabolites Experimental data often on a „gene“ level Multiple genes can produce protein Multiple genes encode one protein Result: many „gene“ values map to one pathway node 10 Alexander Lex | Harvard University C E E1 E2 E3 E4 CA3 KJ2 RAF

11. R IV: Preserving the Layout Pathways are available in carefully designed layouts e.g., KEGG, WikiPathways, Biocarta Users are familiar with layouts Goal: preserve layouts as much as possible Two approaches: Emulate drawing conventions Use original layouts 11 Alexander Lex | Harvard University

12. R V: Supporting Multiple Tasks Two central tasks: Explore topology of pathway Explore the attributes of the nodes (experimental data) Need to support both! 12 Alexander Lex | Harvard University C B D F A E

13. 13

14. Visualization Approaches 14 Separate Linked Views Small Multiples Layout AdaptionLinearization [Meyer 2010] [Junker 2006] Alexander Lex | Harvard University Path-Extraction On-Node Mapping [Lindroos 2002]

15. On-Node Mapping Alexander Lex | Harvard University 15 [Lindroos2002]

16. On-Node Mapping Alexander Lex | Harvard University 16 [Westenberg 2008] [Gehlenborg 2010]

17. On-Node & Tooltip Alexander Lex | Harvard University 17 [Streit 2008]

18. On-Node Mapping Not scalable especially when used with „original“ layout animation not an alternative Good for overview with homogeneous data Excellent for topology-based tasks Bad for attribute-based tasks Alexander Lex | Harvard University 18

19. On-Node Mapping Reflection R I (Scale) bad if working with static layouts limited when working with layout adaption R II (Heterogeneity) bad – can‘t encode multiple datasets R III (Multi-Mapping) bad – can‘t encode multiple mappings Alexander Lex | Harvard University 19

20. On-Node Mapping Reflection R IV (Layout-Preservation) excellent! R V (Multiple Tasks) excellent for topology-based tasks bad for attribute-based tasks Alexander Lex | Harvard University 20

21. [Lindroos 2002] On-Node Mapping Visualization Approaches 21 Small Multiples Layout AdaptionLinearization [Meyer 2010] [Junker 2006] Alexander Lex | Harvard University Path-Extraction Separate Linked Views

22. Alexander Lex | Harvard University 22 [Shannon 2008]

23. Separate Linked Views Alexander Lex | Harvard University 23

24. Separate Linked Views Alexander Lex | Harvard University 24

25. Separate Linked Views Reflection R I (Scale) excellent for large numbers of attributes R II (Heterogeneity) excellent for heterogeneous data e.g., one view per data type R III (Multi-Mapping) good – simple highlighting for multiple elements Alexander Lex | Harvard University 25

26. Separate Linked Views Reflection R IV (Layout-Preservation) excellent! R V (Multiple Tasks) good for topology-based tasks good for attribute-based tasks awful for combining them! Association node-attribute only one by one Alexander Lex | Harvard University 26

27. Separate Linked Views [Lindroos 2002] On-Node Mapping Visualization Approaches 27 Layout AdaptionLinearization [Meyer 2010] [Junker 2006] Alexander Lex | Harvard University Path-Extraction Small Multiples

28. Alexander Lex | Harvard University 28

29. Small Multiples Alexander Lex | Harvard University 29 [Barsky 2008] Video!

30. Small Multiples Reflection R I (Scale) limited to a handful of conditions/experiments differences don‘t „pop out“ R II (Heterogeneity) limited for heterogeneous data e.g., one view per data type R III (Multi-Mapping) bad – no obvious solution Alexander Lex | Harvard University 30

31. Small Multiples Reflection R IV (Layout-Preservation) excellent! R V (Multiple Tasks) good for topology-based tasks limited for attribute-based tasks limited for combining them! comparing one by one -> change blindness Typically requires „focus duplicate“ Alexander Lex | Harvard University 31

32. Separate Linked Views [Lindroos 2002] On-Node Mapping Visualization Approaches 32 Small Multiples Linearization [Meyer 2010] Alexander Lex | Harvard University Path-Extraction Layout Adaption [Junker 2006]

33. Layout Adaption „Moderate“ Layout Adaption make space for on-node encoding Alexander Lex | Harvard University 33 [Gehlenborg 2010] [Junker 2006]

34. Layout Adaption „Extreme“ layout adaption encode information through position Alexander Lex | Harvard University 34 [Bezerianos 2010] Video: http://www.youtube.com/watch?v=NLiHw5B0Mcohttp://www.youtube.com/watch?v=NLiHw5B0Mco

35. Layout Adaption Reflection R I (Scale) limited to a handful of conditions/experiments R II (Heterogeneity) limited for heterogeneous data Different story for „extreme“ layout adaption R III (Multi-Mapping) OK– give nodes with multi-mappings extra space Alexander Lex | Harvard University 35

36. Layout Adaption Reflection R IV (Layout-Preservation) not possible R V (Multiple Tasks) limited for topology-based tasks limited for attribute-based tasks limited for combining them! space for trade-off between topology and attribute tasks Alexander Lex | Harvard University 36

37. Layout Adaption [Junker 2006] Separate Linked Views [Lindroos 2002] On-Node Mapping Visualization Approaches 37 Small Multiples Alexander Lex | Harvard University Path-Extraction Linearization [Meyer 2010]

38. Linearization – Pathline Alexander Lex | Harvard University 38 [Meyer 2010] Combination of layout adaption separate linked views

39. Linearization Alexander Lex | Harvard University 39 [Meyer 2010]

40. Linearization Reflection R I (Scale) good for many experiments R II (Heterogeneity) good for multiple datasets R III (Multi-Mapping) good – give nodes with multi-mappings extra space Alexander Lex | Harvard University 40

41. Linearization Reflection R IV (Layout-Preservation) not possible R V (Multiple Tasks) limited for topology-based tasks limited for attribute-based tasks limited for combining them! Manual creation of linearized version Unclear if suitable for more complex pathways Alexander Lex | Harvard University 41

42. Visualization Approaches 42 On-Node Mapping Separate Linked Views Small Multiples Layout AdaptionLinearization [Meyer 2010] [Junker 2006] [Lindroos 2002] Alexander Lex | Harvard University Path-Extraction

43. Alexander Lex | Harvard University 43

44. Pathway View A E C B D F C B D F A E enRoute View Concept Group 1 Dataset 1 Group 2 Dataset 1 Group 1 Dataset 2 B C F A D E 44 Alexander Lex | Harvard University

45. Pathway View On-Node Mapping Path highlighting with Bubble Sets [Collins2009] Selection Start- and end node Iterative adding of nodes IGF-1 lowhigh 45 Alexander Lex | Harvard University

46. enRoute View – Path Representation Design of KEGG [Kanehisa2008] Abstract branch nodes – Additional topological information – Incoming vs. outgoing branches – Expandable Branch switching 46 Alexander Lex | Harvard University

47. Experimental Data Representation Gene Expression Data (Numerical) Copy Number Data (Ordered Categorical) Mutation Data 47 Alexander Lex | Harvard University

48. enRoute View – Putting All Together 48 Alexander Lex | Harvard University

49. 49 Video! Alexander Lex | Harvard University http://enroute.caleydo.org

50. Glioblastoma Multiforme Example 50 Alexander Lex | Harvard University

51. Glioblastoma Multiforme Example 51 Alexander Lex | Harvard University

52. enRoute Reflection R I (Scale) Excellent, can handle large amounts of data R II (Heterogeneity) Excellent, can handle various datasets R III (Multi-Mapping) Excellent, can resolve multi-mappings without ambiguity Alexander Lex | Harvard University 52

53. enRoute Reflection R IV (Layout-Preservation) Excellent - preserves pathway layout Not preserved in extracted path R V (Multiple Tasks) Good for topology-based tasks High-level topology through pathway view Topology of path in enRoute view Excellent for attribute-based tasks Can handle large, grouped and heterogeneous data Alexander Lex | Harvard University 53

54. Using enRoute enRoute part of Caleydo Biomolecular Visualization Framework http://caleydo.org Caleydo is free for all – open source project More in Marc‘s talk! Alexander Lex | Harvard University 54

55. Alexander Lex | Harvard University 55

56. Which to use? 56 On-Node Mapping Separate Linked Views Small Multiples Layout AdaptionLinearization [Meyer 2010] [Junker 2006] [Lindroos 2002] Alexander Lex | Harvard University Path-Extraction

57. Use Technique that fits your task Topology is important One experimental condition Alexander Lex | Harvard University 57 On-Node Mapping [Lindroos2002]

58. Use Technique that fits your task Topology is important Size of graph is limited Handful of conditions Alexander Lex | Harvard University 58 Small Multiples

59. Use Technique that fits your task Experimental data is critical Pathways are a “sideshow” Alexander Lex | Harvard University 59 [Shannon 2008] Separate Linked Views

60. Use Technique that fits your task Topology & experimental data is important Data is heterogeneous Alexander Lex | Harvard University 60 Path Extraction

61. What’s Nice About That? Caleydo supports all of them ;) Alexander Lex | Harvard University 61

62. Alexander Lex | Harvard University 62

63. Other Pathway-Related Challenges Cross-connections between pathways Alexander Lex | Harvard University 63 [Klukas 2007]

64. Other Pathway-Related Challenges Effect of compounds (medication) on pathways Alexander Lex | Harvard University 64 [Lounkine 2012]

65. Bridging the Gap Between Pathways and Experimental Data Alexander Lex, Harvard University alex@seas.harvard.edu http://caleydo.org ? Marc Streit Hans-Jörg Schulz Christian Partl Dieter Schmalstieg Peter J. Park Nils Gehlenborg Alexander Lex | Harvard University 65