A Geometric Database of Gene Expression Data for the Mouse Brain

1. A Geometric Database of Gene Expression Data for the Mouse Brain Tao Ju, Joe Warren Rice University

2. Overview • Genes are blueprints for creating proteins • For given tissue, only a subset of genes are generating proteins (expressed) • New laboratory method for determining which genes are being expressed (Eichele) • Collect expression data over mouse brain for all 20K genes in mouse genome • Build database of gene expression data

3. Gene Expression Database • Collect gene expression data for small number of cross-sections • Bring 2D cross-sections into 3D alignment using principal component analysis • Deform 3D brain atlas onto aligned cross-sections to account for anatomical deviations • Analyze and compare gene expressions via mapping to standard brain atlas

4. The Standard Mouse Brain • 15 anatomical regions spread over 11 saggital cross-sections (from lateral to medial)

5. Deformable Modeling • Anatomical deviation between mouse brains • Need to deform standard atlas onto each brain • Most deformable models are based on a uniform grid • “Brain Warping”, by Arthur W. Toga • Our contribution: subdivision meshes

6. Subdivision Mesh as Brain Atlas • Subdivision through splitting and averaging • Boundaries of anatomical regions modeled by crease curves • Intersection of three or more regions modeled by crease vertices

7. Demo : Fitting a Mesh

8. Advantages of Subdivision Meshes • Subdivision meshes are easy to fit to image • Simple manual drag-and-drop of control net • Fast automatic fitting methods • Anatomical regions isolated as sub-meshes • Expression data stored as extra coordinate on refined meshes • Allows fast, accurate comparison of data • Multi-resolution structure improves efficiency

9. Automatic Textual Annotation • Previously, biologist examined and manually tagged each anatomical region with pattern and strength of gene expression • Pattern: scattered, regional, ubiquitous • Strength: -, +, ++, +++ • Now, apply filter to determine pattern and strength of expression over sub-mesh corresponding to anatomical region

10. Comparison of Expression Data • Search for an image with the most similar expression pattern to a given target : • Build summaries in each quad at each subdivision level using Haar wavelet • Sort all images by comparing at the coarsest subdivision level into a priority queue • Compare the first image with the target at a finer subdivision level and update the queue, until it is already at the finest level (i.e., a match is found) • Requires monotonic (convex) norm • L1, Chi-square, etc.

11. Geometric Searches • Let the user define a target expression pattern from: • Preset values, • Existing genes. • Selectable distance norm and number of matches.

12. Demo: Searching the Database

13. Accessing Database via the Web • Database of gene expression data and deformed atlases • currently 1207 images from 110 genes. • Web server: www.geneatlas.org • Uploading and viewing gene images. • Fitting standard atlases (using Java Applet). • Graphical interface for searching gene images. • Automatic annotation. • It’s all online!

14. Conclusion • Subdivision meshes for anatomic modeling: • Flexible control allows easy deformation. • Crease points (curves) allows accurate modeling of region boundaries. • Enables fast and accurate comparison between images on the multi-level grid structure.

15. Future Work • Construction of a full 3D deformable atlas of the mouse brain based on hexahedral subdivision meshes. • Algorithms for efficient and accurate fitting of the 3D atlas onto cross-section images. • Enhancement of the searching engine to accept more complicated queries.

16. Collaborators • Baylor College of Medicine • Gregor Eichele, Christina Thaller, Wah Chiu, James Carson • Rice University • David Scott • University of Houston • Ioannis Kakadiaris