BIODATA ANALYSIS                                  &                            VISUALIZATION                              ...
Involved in genomics research:    •chicken, cow, human genome DNA sequencing    •search for genetic variation responsible ...
A. Filtering                          Investigating parameter space...Tuesday 1 February 2011
putative mutations       filter 1       filter 2       filter 3                          A         B                 C       ...
What we find...                                          B                             A                                   ...
What we find...                                                B                             A                             ...
What we should have found...                                  B                          A                              CT...
parameter-spaceTuesday 1 February 2011
Tuesday 1 February 2011
Tuesday 1 February 2011
Tuesday 1 February 2011
sometimes: bypass parameter-settingTuesday 1 February 2011
Tuesday 1 February 2011
Tuesday 1 February 2011
Tuesday 1 February 2011
Aim: use interactive visualization of the “raw” data        to:    •peep inside the black box    •get feel for the data   ...
Aim: use interactive visualization of the “raw” data        to:    •peep inside the black box                 di sease    ...
B. Pattern searching                          Making sense of our data...Tuesday 1 February 2011
Tuesday 1 February 2011
Tuesday 1 February 2011
Typical example: gene networks                           => can we identify patterns?                           same      ...
How do these networks differ?Tuesday 1 February 2011
Hive Plots, taken from http://mkweb.bcgsc.ca/linnet/Tuesday 1 February 2011
Aim: help researchers make sense of complicated        data:    • gene                networks    • structural            ...
Aim: help researchers make sense of complicated        data:                                                       dise as...
Hurdles:    • big         data (millions/billions of datapoints)            => makes interactivity difficult            sol...
ToolsTuesday 1 February 2011
User groups:        researcher => clinician => patientTuesday 1 February 2011
Bioinformatics                          VisualizationTuesday 1 February 2011
So:    •visual analytics: visually identifying patterns in large        datasets to inform on statistical analysis    •use...
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LICT Human-Machine-Interface

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LICT Human-Machine-Interface

  1. 1. BIODATA ANALYSIS & VISUALIZATION Jan Aerts Faculty of Engineering - ESAT/SCD http://saaientist.blogspot.com @jandotTuesday 1 February 2011
  2. 2. Involved in genomics research: •chicken, cow, human genome DNA sequencing •search for genetic variation responsible for phenotype/ disease Issues with •filtering: finding the correct set of parameters •pattern searching: grasping the significance and effect of the mutations => visual analyticsTuesday 1 February 2011
  3. 3. A. Filtering Investigating parameter space...Tuesday 1 February 2011
  4. 4. putative mutations filter 1 filter 2 filter 3 A B C different settings for filtersTuesday 1 February 2011
  5. 5. What we find... B A CTuesday 1 February 2011
  6. 6. What we find... B A C State of the art: run many filter pipelines and take intersectionTuesday 1 February 2011
  7. 7. What we should have found... B A CTuesday 1 February 2011
  8. 8. parameter-spaceTuesday 1 February 2011
  9. 9. Tuesday 1 February 2011
  10. 10. Tuesday 1 February 2011
  11. 11. Tuesday 1 February 2011
  12. 12. sometimes: bypass parameter-settingTuesday 1 February 2011
  13. 13. Tuesday 1 February 2011
  14. 14. Tuesday 1 February 2011
  15. 15. Tuesday 1 February 2011
  16. 16. Aim: use interactive visualization of the “raw” data to: •peep inside the black box •get feel for the data •get feel for how filter settings influence each otherTuesday 1 February 2011
  17. 17. Aim: use interactive visualization of the “raw” data to: •peep inside the black box di sease radic ate •get feel for the data E •get feel for how filter settings influence each otherTuesday 1 February 2011
  18. 18. B. Pattern searching Making sense of our data...Tuesday 1 February 2011
  19. 19. Tuesday 1 February 2011
  20. 20. Tuesday 1 February 2011
  21. 21. Typical example: gene networks => can we identify patterns? same networkTuesday 1 February 2011
  22. 22. How do these networks differ?Tuesday 1 February 2011
  23. 23. Hive Plots, taken from http://mkweb.bcgsc.ca/linnet/Tuesday 1 February 2011
  24. 24. Aim: help researchers make sense of complicated data: • gene networks • structural variation in the genome • linked data • ...Tuesday 1 February 2011
  25. 25. Aim: help researchers make sense of complicated data: dise ase • gene networks radic ate E • structural variation in the genome • linked data • ...Tuesday 1 February 2011
  26. 26. Hurdles: • big data (millions/billions of datapoints) => makes interactivity difficult solution: indexing methods, data formats, dimensionality reduction, ... • visual encodingTuesday 1 February 2011
  27. 27. ToolsTuesday 1 February 2011
  28. 28. User groups: researcher => clinician => patientTuesday 1 February 2011
  29. 29. Bioinformatics VisualizationTuesday 1 February 2011
  30. 30. So: •visual analytics: visually identifying patterns in large datasets to inform on statistical analysis •use visualization to make sense of complex dataTuesday 1 February 2011
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