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

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

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

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

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

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

Demo : Fitting a Mesh

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

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

Comparison of Expression Data Search for an image with the most similar expression pattern to a given target : 1.Build summaries in each quad at each subdivision level using Haar wavelet 2.Sort all images by comparing at the coarsest subdivision level into a priority queue 3.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.

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

Demo: Searching the Database

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

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.

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.

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