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Bridging the Gap Between Pathways and Experimental Data

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

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  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. Alexander Lex | Harvard University

  3. Why use Visualization? • Efficient communication of information • A -3.4 • B 2.8 • C 3.1 • D -3 • E 0.5 • F 0.3 B A C D E F Alexander Lex | Harvard University

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

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

  6. Requirements Analysis 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 Alexander Lex | Harvard University

  8. R I: Data Scale • Large number of experiments • Large datasets have more than 500 experiments • Multiple groups/conditions 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 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 CA3 C KJ2 RAF E1 E2 E E3 E4 Alexander Lex | Harvard University

  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 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! B A C D E F Alexander Lex | Harvard University

  13. Visualization Techniques Alexander Lex | Harvard University

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

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

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

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

  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

  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

  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

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

  22. Separate Linked Views [Shannon 2008] Alexander Lex | Harvard University

  23. Separate Linked Views Alexander Lex | Harvard University

  24. Separate Linked Views Alexander Lex | Harvard University

  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

  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

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

  28. Small Multiples Alexander Lex | Harvard University

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  43. Caleydo EnRoute Alexander Lex | Harvard University

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

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

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

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

  50. Glioblastoma Multiforme Example Alexander Lex | Harvard University