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SP2.3: UI and VR Based Visualization

Collaborative research project focusing on developing advanced visualization methods for mass spectrometry data analysis, emphasizing spectral feature alignment and ion cloud dynamics. The project aims to enhance protein identification in proteomics and improve mass accuracy understanding.

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SP2.3: UI and VR Based Visualization

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  1. SP2.3: UI and VR Based Visualization Partners: TU Delft, VU, CWI • Ongoing Activities and progress • Collaboration Highlight with SP 1.6 DUTELLA R. van Liere April 7th, 2006

  2. SP 2.3 people • 4 PhD students: • Broersen, Burakiew, Kruszynski (CWI) • van der Schaaf (VU) • 3 PD: • Botha, Koutek (TUD) • de Leeuw (CWI) • 4 supervision: • van Liere (CWI) • Post, Jansen (TUD) • Bal (VU)

  3. SP2.3 ongoing activities • Multi-spectral visualization SP 1.6 • Particle visualization SP 1.6 • Confocal Cell Imaging • Volume measuring SP 2.1 • Medical Imaging SP 1.4 • Virtual Reality on the GRID SP 3.1 • Distributed Scene Graphs SP 3.1

  4. SP 2.3 status • 25 international publications • 2 spin-offs • Foldyne (TU Delft) • Personal Space Technologies (CWI) • Projected output • 4 PhD thesis • At least 2 packages in PoC

  5. Collaboration SP 1.6 DUTELLA • Prof Ron Heeren (ALMOF) • Topic: Mass Spectrometry for molecular imaging • Motivation: need for better MS analysis tools • Visualization Topics: • Multi-spectral data visualization • In-silico mass spectrometry • Envisioned output: • GRID enabled toolbox for MS analysis • Applications according to VL-e methodology

  6. Problem: aligning multi-spectral data cubes • Multi-spectral data cube: 256x256x65k • Multiple data cubes • ±100 cubes in mosaic • Current procedure: manual alignment on pixel values

  7. Our novel approach • Idea: Align spectral features in adjacent samples • Approach: • Compute spectral features using PCA • For each feature, find a most optimal spatial alignment of the feature • The overall spatial alignment is optimal for all features

  8. MS beelden dijbeen muis

  9. First Spectral Feature = Principal Component1

  10. Second Spectral Feature Principal Component2

  11. use the combination of 2 local minima Minimization map of 2nd feature Minima landscape Minimization map of 1st feature

  12. Impact ? Generic ? GRID? • Faster, unsupervised objective reproducible alignment combined with VL inspection tools for SP1.6 • Method can also be applied to multi-spectral data cubes from other types of microscopes/telescopes. • Data-cube:256x256x65K. 100 cubes. Alignment:15min in Matlab. Combinations: (100 2) * 15

  13. Problem: Meaningful ion dynamics • Ion clouds: ~50k ions x 1M steps • Current visualizations are low level, eg.: • But how about: • Intra ion-cluster interactions and their causes • Intra ion-cluster interactions?

  14. Our novel approach • Idea: simplify images with • Statistical parameterized icons • Semantic camera control • Approach: • Parameterized “comet-icons” • Camera motion relative to comet dynamics

  15. Example: icons • Ions groups • Statistical ion properties of group • Ion density dynamics

  16. Example: camera control • Trapping motion • Relative cyclotron frequency • Tracks of Frenet frames

  17. Impact ? Generic ? GRID? • Improvement of mass accuracy understanding/control leads to enhanced protein ID in proteomics • Software framework is targeted towards particle visualization. Semantics of icons/cameras can be added/changed/enhanced • Near-future: optimization of simulation initial conditions

  18. Final SP 2.3 comments • SP 2.3 is well on track • Projected output: • GRID enabled toolbox SP2 layer • Applications using toolbox SP1 layer • However: visualization PhDs are not mass spectrometry scientists!

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