Visualization Approaches for Biomedical Omics Data: Putting It All Together
Keynote Talk presented at the 1st Annual BiVi Community Annual Meeting (17 December 2014) http://bivi.co/page/bivi-annual-meeting-16-17th-december-2014 Visualization Approaches for Biomedical Omics Data: Putting It All Together The rapid proliferation of high quality, low cost genome-wide measurement technologies such as whole-genome and transcriptome sequencing, as well as advances in epigenomics and proteomics, are enabling researchers to perform studies that generate heterogeneous datasets for cohorts of thousands of individuals. A common feature of these studies is that a collection of genome-wide, molecular data types and phenotypic or clinical characterizations are available for each individual. These data can be used to identify the molecular basis of diseases and to characterize and describe the variations that are relevant for improved diagnosis, prognosis and targeted treatment of patients. An example for a study in which this approach has been successfully applied is The Cancer Genome Atlas project (http://cancergenome.nih.gov). In my talk I will discuss how visualization approaches can be applied to enable exploration and support analysis of data generated by such studies. Specifically, I will review techniques and tools for visual exploration of individual omics data types, their ability to scale to large numbers of individuals or samples, and emerging techniques that integrate multiple omics data types for interactive visual analysis. I will also examine technical and legal challenges that developers of such visualization tools are facing. To conclude my talk, I will outline research opportunities for the biological data visualization community that address major challenges in this domain.
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Visualization Approaches for Biomedical Omics Data: Putting It All Together
- 1. Visualization Approaches for Biomedical Omics Data: Putting It All Together Nils Gehlenborg Harvard Medical School Center for Biomedical Informatics !nils_gehlenborg
- 2. Why am I doing this?
- 3. Human " # Machine
- 4. Human " INTERPRETATION Data COMPUTATION GENERATION # Machine
- 5. “In every chain of reasoning, the evidence of the last conclusion can be no greater than that of the weakest link of the chain, whatever may be the strength of the rest.” - Thomas Reid, Essays on the Intellectual Powers of Man (1786)
- 6. Human " INTERPRETATION Data COMPUTATION GENERATION # Machine Hypotheses Discoveries Knowledge Cognition
- 7. x y1 y2 1.00 0.96 0.76 2.00 0.76 -0.14 3.00 -0.14 -0.91 4.00 -0.91 -0.84 5.00 -0.84 0.00 6.00 0.00 0.84 7.00 0.84 0.91 8.00 0.91 0.14 9.00 0.14 -0.76 10.00 -0.76 -0.96 11.00 -0.96 -0.28
- 8. 1 0.5 0 -0.5 -1 1 2 3 4 5 6 7 8 9 10 11
- 9. Human INTERPRETATION | Data COMPUTATION GENERATION Machine Hypotheses Discoveries Knowledge " # Cognition
- 10. What are the data?
- 13. DNA Icon by Darrin Higgins, from The Noun Project
- 14. Genome What is the DNA sequence?
- 15. Genome HOW? sequencing of genomic DNA WHAT? single nucleotide variants (SNVs) copy number variants complex structural variants
- 18. Clark et al. 2009, PLoS Genetics http://circos.ca, Krzywinski et al. 2009, Genome Research
- 19. source: Human destination: Lizard chr1 chr2 chr3 chr4 chr5 chr21 chr16 chr17 chr18 chr19 chr20 chr22 chrX chrY chr3 chr1 chr5 chrb chrd chrg chr15 chr14 chr4 chr13 chr2 chr12 chr6 chr3 chr11 chr7 chr10 chr8 chr9 chr6 chra chrc chrf chrh line saturation - + 10Mb chr3 go to: chr3 chr2 51280143 152008850 51709189 152345239 out in orientation: invert match inversion http://www.mizbee.org
- 22. Genome Transcriptome What is the DNA sequence? Which genes are active?
- 23. Transcriptome HOW? sequencing of mRNA/microRNA molecules microarray-based hybridization WHAT? abundance of transcripts/genes/isoforms
- 27. Genome Transcriptome Proteome What is the DNA sequence? Which genes are active? Which proteins are present?
- 28. Proteome HOW? mass spectrometry of peptides array-based techniques WHAT? presence of peptides & proteins abundance of peptides & proteins
- 30. Genome Transcriptome Proteome Metabolome What is the DNA sequence? Which genes are active? Which proteins are present? Which metabolites can be identified?
- 31. Metabolome HOW? mass spectrometry NMR spectroscopy WHAT? presence of metabolites abundance of metabolites
- 32. Genome Transcriptome Proteome Metabolome What is the DNA sequence? Which genes are active? Which proteins are present? Which metabolites can be identified? Interactome Which molecules are interacting?
- 33. Interactome HOW? mass spectrometry, yeast-2-hybrid text mining WHAT? links between molecules
- 34. Epigenome Genome Transcriptome Proteome Metabolome How are DNA and associated proteins modified? What is the DNA sequence? Which genes are active? Which proteins are present? Which metabolites can be identified? Interactome Which molecules are interacting?
- 35. Epigenome HOW? ChIP-seq, ChIP-chip (histones modifications) bisulfite sequencing (DNA methylation) WHAT? histone modifications along genome DNA methylation patterns along genome
- 39. Nucleome Epigenome Genome Transcriptome Proteome Metabolome How is the DNA organized in space/time? How are DNA and associated proteins modified? What is the DNA sequence? Which genes are active? Which proteins are present? Which metabolites can be identified? Interactome Which molecules are interacting?
- 40. Nucleome HOW? 3C/4C/5C chromosome conformation capture Hi-C sequencing WHAT? contact probabilities for different parts of the genome
- 41. Lieberman-Aiden et al., Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome, 2009
- 42. Scaling
- 43. Dimensions Genomic Entities Data Types Samples Timepoints
- 44. ?
- 45. TCGA The Cancer Genome Atlas
- 48. mRNA expression microRNA expression DNA methylation protein expression copy number variants mutation calls clinical parameters
- 54. Stratome
- 55. Anthony92931 / Wikimedia Commons + Modification by Nils Gehlenborg
- 58. StratomeX M Streit, A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, PJ Park, N Gehlenborg, “Guided Visual Exploration of Genomic Stratifications in Cancer“, Nature Methods 11:884-885 (2014) A Lex, M Streit, H-J Schulz, C Partl, D Schmalstieg, PJ Park, N Gehlenborg, “StratomeX: Visual Anal-ysis of Large-Scale Heterogeneous Genomics Data for Cancer Subtype Characterization“, Computer Graphics Forum 31:1175-1184 (2012)
- 60. Is there a mutation that overlaps with this mRNA cluster? Is there a mutually exclusive mutation? Is there a CNV that affects survival? Is there a pathway that is enriched in this cluster? Guided Exploration Query Rank Visualize Stratifications Clinical Params Pathways
- 62. demand products
- 65. genome browser network viewer heatmap visualization users visualization tools
- 67. genome browser network viewer heatmap visualization users visualization tools
- 68. users visualization tools customized tools for very specific problems
- 69. Meyer et al., “MulteeSum: A Tool for Comparative Spatial and Temporal Gene Expression Data”, 2010
- 70. users visualization tools customized tools for very specific problems
- 71. What am I doing?
- 72. Building infrastructure to build visualization tools.
- 73. Visualization Tools Visualization Components Visualization Libraries Data Analytics Data Management
- 74. Visualization Tools Visualization Components Visualization Libraries Data Analytics Data Management Abstraction
- 75. System/Platform Visualization Tools Visualization Components Visualization Libraries Data Analytics Data Management Abstraction
- 76. Think about scale. Think about systems.
- 77. Acknowledgements Harvard SEAS Alexander Lex, Hanspeter Pfister MD Anderson Cancer Center University of Rostock Psalm Haseley, Richard Park, Peter J Park Michael S Noble, Douglas Voet, Lihua Zou, Spring Liu, Hailei Zhang, Sachet Shukla, Aaron McKenna, Andrew Cherniak, Pei Lin, Gad Getz Jianhua Zhang, Terrence Wu, Ian Watson, Steven Quayle, Lynda Chin Harvard Medical School Broad Institute of MIT & Harvard Graz University of Technology Christian Partl, Dieter Schmalstieg Johannes Kepler University Linz Samuel Gratzl, Stefan Luger, Marc Streit Hans-Jörg Schulz Harvard School of Public Health Funding NIH/NHGRI K99 HG007583 Ilya Sytchev, Shannan Ho Sui, Winston Hide