4D Gene Expression Mapping in Mouse Brain: Visual Analysis and Gene Category Search

1. Order of Speakers Project Proposal - CS3610 (Spring 2013) Abed Dan Walker Yu Du Anatoli Teng Brian Becca Adrian Tim Sean Xiangmin Koonwah Yuriy Ben Xiang • John

2. Abed

3. Gnome Wide 4D Gene Expression Mapping in the Mouse Brain High-throughput method enables genome-wide gene expression search Visual analysis of this data is a challenging task

4. Sci Vis Contest (4D Gene Expression Mapping) • Temporal dimension • Comparison between different levels is non-trivial

5. Mouse Brain Data • ~12000 expression energy volumes • 6 developmental stages • ~2000 genes • 6 annotated volumes • 6 reference atlas volumes • Structure level energy values • A hierarchical time-varying structure • 11 gene categories

6. Challenges Gradient Identification Structural Patterns: Which genes show strong expression in a small set of structures but little expression elsewhere? How do these patterns change throughout development? Which categories do these genes belong to? Structure Consistency Complementary Patterns

7. Related Work Ng LL et. al. 2012 Ng LL et.al. 2007

8. Proposed Approach • Which genes show strong expression in a small set of structures? • Semi-automatic algorithm development for data analysis • Visual query using infovis techniques • How do these patterns change throughout development? • Filtering and interactive visualization of pattern change • Which categories do these genes belong to?

9. Project Time-line 1st week: Developing scripts for querying the data 2nd week: Algorithm development for searching genes based on expression energy 3rd week: Augmenting the algorithm with interactive visualization and visual queries 4-6th week: Extending the algorithm and visualization techniques to include temporal dimension 7th week: Gene category search. 8th week: Wrapping up and report writing

10. Dan Walker

11. Automated Axes Ordering in Parallel Coordinates A Project Proposal by Daniel Walker

12. Background • Parallel Coordinates • Support multiple dimensions • Emphasize attribute ranges • Present all data values • Display correlations Public domain image, courtesy Wikipedia

13. Problem • Order matters • Good neighbors can be hard to find! • Correlation relies on patterns • Not inherently ordered • Currently manual task Public domain image, courtesy Wikipedia

14. Prior Work • Arrangement • Alfred Inselberg. Parallel Coordinates: Visual Multidimensional Geometry and Its Applications. Springer Science+Business Media, 2009. • Categorical Data • Sheng Ma and Joseph Hellerstein.Ordering Categorical Data to Improve Visualization. IEEE 2009. • Quantitative Data and Arrangement • MihaelAnkerst, Stefan Berchtold, and Daniel Keim. Similarity Clustering of Dimensions for an Enhanced Visualization of Multidimensional Data.

15. Solution • Score attribute similarity (statistics) • Imagine line intersections for quantitative values • Maximize total score when selecting ordering • Allows some attributes to be fixed Public domain image, courtesy Wikipedia

16. Timeline • March 11 - 22: Implement parallel coordinates visualization program • March 25 - April 5: Integrate automated ordering • April 8 - 12: User study • April 15 - 26: Paper and report preparation Public domain image, courtesy Wikipedia

17. Questions

18. Yu Du

19. Workload Memory Write Traffic Characterization and Visualization Yu Du

20. Background • Computer Architecture Make software run faster and more efficiently on hardware • Understand Workload Behavior • Identify performance bottlenecks • Find improvement potentials • Visualization is a powerful tool

21. Problem • Non-volatile Memory • NAND , Phase-change Memory (PCM), STT-RAM, ReRAM, … • Hybrid Memory • DRAM: High-performance, High standby power • Non-volatile Memory: Low standby power, Poor Write Performance • Data Placement Problem (Limited DRAM Capacity) • Understand Workload Memory Write Traffic • Find a better data placement strategy • New visualization tool?

22. Time Series Data • Collect Memory Write Traffic (Simics, PIN) • Data Format Cycle + Inst + Addr + WriteSig WriteSig: # of modified bits per word • Data Aggregation • 500M write records (16GB per workload) • Time Dimension • Address Space Dimension • By address (page, superpage) • By write signature (clustering?)

23. Method • Data Aggregator Module • 16GB trace file less than 500MB intermediate files • Profiling • Multiple Post-Processing Modules • One module per analysis • 500MB  one XML output per module • Visualization • XML  D3  SVG

24. Deliverables • Workload Write Dashboard • Multiple graphs on one page • Which data is being written most? When? • Footprint (Size), Intensity, # of modified bits per write … • Write Traffic Dynamics • Sustained Write, Burst Write and Sporadic Write • Write Intensity over Time (3D-plot) • Mixed Workload Analysis • Importance of a workload in a set of workloads

25. Thank you

26. Anatoli

27. Maps (Terrain + Bicycling)

29. Linked Views

30. Elevation encoded with color

31. Tools I will use: • - The Google Elevation API (provides elevation data)

32. Teng

33. Visualization Project Proposal Mouse Brain Gene Expression Teng Han

34. Motivation • SciVis 2013 Contest • Challenge (the 2nd) • Identify structural level genes’ expression strength • Which genes included? • How the expression pattern changes through different steps? • Which categories do the genes belong to?

35. Related Work • Butterfly http://www.hhmi.org/biointeractive/evolution/Visualizing_Gene_Expression/01.html

36. Related Work • Brain Explorer http://www.biomedcentral.com/1471-2105/9/153#B11 • C++ and OpenGL1.1 API

37. Approach • Data • 6 stages * ~2000 genes • Structural –level expression energy values for all genes • Hierarchical, time-varying structure ontology • 11 gene categories, associated with various genes

38. Approach • Visualization • Hierarchies plus Statistical distribution • Structures are time-varying • Genes have multiple probes • Tools • Processing + …

39. Time-Line

40. Deliverables • A runnable program that visualize • Structures at different stages • Genes expressing in each structure • Categories of those expressing genes • Expression pattern changes for a structure through different steps

41. Suggestions

42. Brian

43. Visualization of Agent Interaction in Behavior Based Disease Transmission Simulation Brian Dicks 1/6

44. Motivation • Disease transmission simulators may use color to indicate state of individual • Susceptible, exposed, infected, recovered (immune) • Events are not usually modeled • It’s not very obvious where someone was infected and which agent was responsible • How might we highlight interaction events • Sneezing, state change 2/6

45. Prior Research • Zhou, Gong, and Li presented a human daily behavior model for researching SARS transmission • Agents go to work, supermarket • Based on discrete grid • Color change indications infection 3/6

46. Proposed Approach • Exposure bubbles • Animations to show state transition • Blink different color • Video game style animation • Show ‘sneezing’ agent, exposed agents • Have him sneeze • Agents blink to show exposure • Sort of like an RPG • Will use OpenGL • Lua for scripts • May use an AI toolkit (SWARM) 4/6

47. Eight Week Timeline • Weeks 1-2: Have basic visualization primitives implemented (display characters, animation) • Week 3: Embed Lua interpreter • Week 4: Implement basic AI; have agents go to work, etc • Week 5: Implement pathfinding • Week 6: Implement interactions • Week 7: Show animations in response to interactions • Week 8: Work on report, polish, bug fixes 5/6

48. End of Presentation • Questions/Comments? 6/6

49. Becca

50. Visualization of Protein Mutations & Function Rebecca Hachey BioVis 2013 Data Contest