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: 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!

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