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EMBL John Kendrew Award Lecture 2018

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Receiving the John Kendrew Award is a great honour for me, and I am humbled to be joining the ranks of the previous recipients. None of this would have been possible without the many people who influenced my career at EMBL and Harvard Medical School, in particular, my past and present mentors. To me, the John Kendrew Award is not only a recognition of my achievements. I also consider it an acknowledgment of the importance of my field—visualisation of biomedical data—which was in its infancy when I started my PhD at the EMBL-EBI in 2006.

https://www.embl.de/aboutus/alumni/news/news_2018/20180302_gehlenborg/index.html

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EMBL John Kendrew Award Lecture 2018

  1. 1. Nils Gehlenborg, PhD Department of Biomedical Informatics Harvard Medical School @ngehlenborg
  2. 2. Nils Gehlenborg, PhD Department of Biomedical Informatics Harvard Medical School @ngehlenborg + = ?
  3. 3. My Time at EMBL-EBI
  4. 4. Visualization and Exploration of Transcriptomics Data
  5. 5. HYPOTHESIS
  6. 6. experiment DATA HYPOTHESIS
  7. 7. experiment DATA INSIGHT HYPOTHESIS interpretation
  8. 8. experiment DATA INSIGHT HYPOTHESIS interpretation hypothesis generation
  9. 9. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation hypothesis generation
  10. 10. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation hypothesis generation EXPLANATION
  11. 11. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation hypothesis generation EXPLANATION “Storytelling”
  12. 12. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation hypothesis generation EXPLORATION EXPLANATION “Storytelling”
  13. 13. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation hypothesis generation EXPLORATION EXPLANATION “Storytelling” “Pattern Discovery”
  14. 14. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation HYPOTHESIS hypothesis generation EXPLORATION EXPLANATION “Storytelling” “Pattern Discovery” HYPOTHESIS-DRIVEN DISCOVERY
  15. 15. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation DATA hypothesis generation EXPLORATION EXPLANATION “Storytelling” “Pattern Discovery” DATA-DRIVEN DISCOVERY
  16. 16. REPORT experiment DATA INSIGHT HYPOTHESIS interpretation DATA hypothesis generation EXPLORATION EXPLANATION “Storytelling” “Pattern Discovery” DATA-DRIVEN DISCOVERY
  17. 17. Database
  18. 18. Database Data Set
  19. 19. Database Data Set
  20. 20. Database Data Set
  21. 21. Database Data Set
  22. 22. ?
  23. 23. ?
  24. 24. ? Shoe salesperson - an expert to find the shoes you are looking for
  25. 25. ? Shoe salesperson - an expert to find the shoes you are looking for Display rack - a means to display the content of the shoe boxes
  26. 26. ?
  27. 27. Space Maps Multi-Resolution Glyphs Gehlenborg and Brazma, BMC Bioinformatics, 2009
  28. 28. Space Maps Real World Example low high Human Gene Expression Map SNAP-25 Level 1 1 node Level 2 4 nodes Level 3 15 nodes Level 4 371 nodes Level 5 5,372 nodes Gehlenborg and Brazma, BMC Bioinformatics, 2009
  29. 29. Space Maps Arrangement of Glyphs Grid Projection NeRV Knowledge-driven Data-driven Data-driven Gehlenborg and Brazma, BMC Bioinformatics, 2009
  30. 30. REX Topic Model Blei et al. (2003); Caldas, Gehlenborg et al. (2009) infer Topic Model comparison: list of gene sets w/ counts T T T GS GS GS GS comparison: C distribution over distributions over Caldas, Gehlenborg, et al., Bioinformatics, 2009 Retrieval of Relevant Experiments
  31. 31. REX Topic Model Blei et al. (2003); Caldas, Gehlenborg et al. (2009) infer Topic Model comparison: list of gene sets w/ counts T T T GS GS GS GS comparison: document: document: words C W W W W distribution over distributions over (for text documents) D “Car” driver engine driving tires mileage Caldas, Gehlenborg, et al., Bioinformatics, 2009 Retrieval of Relevant Experiments
  32. 32. REX Caldas, Gehlenborg, et al., Bioinformatics, 2009 Retrieval of Relevant Experiments
  33. 33. Moving on to Harvard Medical School
  34. 34. The Cancer Genome Atlas 10,000+ patients 20+ tumor types
  35. 35. The Cancer Genome Atlas 10,000+ patients 20+ tumor types
  36. 36. mRNA expression The Cancer Genome Atlas 10,000+ patients 20+ tumor types
  37. 37. microRNA expression mRNA expression The Cancer Genome Atlas 10,000+ patients 20+ tumor types
  38. 38. microRNA expression protein expression mRNA expression The Cancer Genome Atlas 10,000+ patients 20+ tumor types
  39. 39. microRNA expression protein expression mutation calls mRNA expression The Cancer Genome Atlas 10,000+ patients 20+ tumor types
  40. 40. microRNA expression protein expression copy number variants mutation calls mRNA expression The Cancer Genome Atlas 10,000+ patients 20+ tumor types
  41. 41. microRNA expression DNA methylation protein expression copy number variants mutation calls mRNA expression The Cancer Genome Atlas 10,000+ patients 20+ tumor types
  42. 42. 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
  43. 43. mRNA expression
  44. 44. C4C3C2C1 mRNA expression clustering Tumor Subtypes
  45. 45. C4C3C2C1 LONGER TYPICAL SHORTER patient survival time mRNA expression clustering Tumor Subtypes
  46. 46. C4C3C2C1 LONGER TYPICAL SHORTER WILDTYPEMUT patient survival time mutation status of gene Y mRNA expression clustering Tumor Subtypes
  47. 47. C4C3C2C1 WILDTYPEMUT mRNA expression clustering patient survival time mutation status of gene Y Tumor Subtypes LONGER TYPICAL SHORTER
  48. 48. C4C3C2C1 WILDTYPEMUT mRNA expression clustering patient survival time mutation status of gene Y Tumor Subtypes LONGER TYPICAL SHORTER
  49. 49. C4C3C2C1 WILDTYPEMUT mRNA expression clustering patient survival time mutation status of gene Y Tumor Subtypes LONGER TYPICAL SHORTER
  50. 50. C4C3C2C1 WILDTYPEMUT patient survival time mutation status of gene Y mRNA expression clustering Tumor Subtypes LONGER TYPICAL SHORTER
  51. 51. StratomeX
  52. 52. StratomeX PROBLEM 1 Visualize overlap of patient sets across two or more stratifications.
  53. 53. StratomeX PROBLEM 1 Visualize overlap of patient sets across two or more stratifications. PROBLEM 2 Visualize characteristics of patient sets within a stratification of interest.
  54. 54. M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, P Park, N Gehlenborg , Nature Methods, 2014
  55. 55. StratomeX PROBLEM 1 Visualize overlap of patient sets across two or more stratifications. PROBLEM 2 Visualize characteristics of patient sets within a stratification of interest.
  56. 56. StratomeX 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.
  57. 57. 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)
  58. 58. 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)
  59. 59. 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)
  60. 60. Is there a mutation that overlaps with this mRNA cluster? 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)
  61. 61. Is there a mutation that overlaps with this mRNA 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)
  62. 62. Is there a mutation that overlaps with this mRNA cluster? Is there a CNV that affects survival? 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)
  63. 63. 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)
  64. 64. Query Rank 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)
  65. 65. 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)
  66. 66. Dekker et al., Nature, 2017
  67. 67. Genome Interaction Data
  68. 68. Genome Interaction Data PROBLEM 1 Visualize a matrix of 3 million x 3 million cells across multiple scales
  69. 69. Genome Interaction Data PROBLEM 1 Visualize a matrix of 3 million x 3 million cells across multiple scales PROBLEM 2 Support comparison of many different conditions
  70. 70. Kerpedjiev et al., biorxiv, 2018; http://higlass.io/app/?config=TKXaqsSIRvGEcw2dAUQvxg
  71. 71. Kerpedjiev et al., biorxiv, 2018; http://higlass.io/app/?config=TKXaqsSIRvGEcw2dAUQvxg
  72. 72. Kerpedjiev et al., biorxiv, 2018; Schwarzer et al., Nature, 2017
  73. 73. Kerpedjiev et al., biorxiv, 2018; Schwarzer et al., Nature, 2017
  74. 74. Kerpedjiev et al., biorxiv, 2018; Schwarzer et al., Nature, 2017
  75. 75. Kerpedjiev et al., biorxiv, 2018; Schwarzer et al., Nature, 2017
  76. 76. http://higlass.io/app/?config=dyE970c4TH21onnRvT1PmQ
  77. 77. http://higlass.io/app/?config=dyE970c4TH21onnRvT1PmQ
  78. 78. http://higlass.io/app/?config=dyE970c4TH21onnRvT1PmQ
  79. 79. Lekschas et al., Transactions on Visualization and Computer Graphics, 2018
  80. 80. Lekschas et al., Transactions on Visualization and Computer Graphics, 2018
  81. 81. Lekschas et al., Transactions on Visualization and Computer Graphics, 2018
  82. 82. Lekschas et al., Transactions on Visualization and Computer Graphics, 2018
  83. 83. Lekschas et al., Transactions on Visualization and Computer Graphics, 2018
  84. 84. Lekschas et al., Transactions on Visualization and Computer Graphics, 2018
  85. 85. Beyond the Lab
  86. 86. EDUCATION Nature Methods Points of View; courses and workshops
  87. 87. OPEN SCIENCE Preprints for 4D Nucleome Consortium
  88. 88. OPEN SCIENCE Preprints for 4D Nucleome Consortium
  89. 89. COMMUNITY BUILDING VIZBI, BioVis, and other meetings
  90. 90. A Tale of Great Perseverance
  91. 91. Thank You!
  92. 92. EMBL Alumni Association Kay Nieselt Alvis Brazma Nicholas Luscombe Gos Micklem Lars Steinmetz Peter J Park Isaac Kohane Alexander Lex Marc Streit Hanspeter Pfister Peter Kerpedjiev Fritz Lekschas Sabrina Nusrat Jennifer Marx Scott Ouellette Chuck McCallum Theresa Harbig Danielle Nguyen Jake Conway Undina Gisladottir Maureen Ruby and Clara
  93. 93. EMBL Alumni Association Kay Nieselt Alvis Brazma Nicholas Luscombe Gos Micklem Lars Steinmetz Peter J Park Isaac Kohane Alexander Lex Marc Streit Hanspeter Pfister Peter Kerpedjiev Fritz Lekschas Sabrina Nusrat Jennifer Marx Scott Ouellette Chuck McCallum Theresa Harbig Danielle Nguyen Jake Conway Undina Gisladottir Maureen Ruby and Clara

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