This article is part of the supplement: Neural Information Processing Systems (NIPS) workshop on New Problems and Methods in Computational Biology
Analyzing in situ gene expression in the mouse brain with image registration, feature extraction and block clustering
1 Department of Computer Science, University of Massachusetts Amherst, Amherst, MA-01003, USA
2 Department of Computer Science, University of Rochester, Rochester, NY-14627, USA
3 Department of Biology & The Laboratory of Molecular and Cellular Neurobiology, University of Massachusetts Amherst, Amherst, MA-01003, USA
BMC Bioinformatics 2007, 8(Suppl 10):S5 doi:10.1186/1471-2105-8-S10-S5Published: 21 December 2007
Many important high throughput projects use in situ hybridization and may require the analysis of images of spatial cross sections of organisms taken with cellular level resolution. Projects creating gene expression atlases at unprecedented scales for the embryonic fruit fly as well as the embryonic and adult mouse already involve the analysis of hundreds of thousands of high resolution experimental images mapping mRNA expression patterns. Challenges include accurate registration of highly deformed tissues, associating cells with known anatomical regions, and identifying groups of genes whose expression is coordinately regulated with respect to both concentration and spatial location. Solutions to these and other challenges will lead to a richer understanding of the complex system aspects of gene regulation in heterogeneous tissue.
We present an end-to-end approach for processing raw in situ expression imagery and performing subsequent analysis. We use a non-linear, information theoretic based image registration technique specifically adapted for mapping expression images to anatomical annotations and a method for extracting expression information within an anatomical region. Our method consists of coarse registration, fine registration, and expression feature extraction steps. From this we obtain a matrix for expression characteristics with rows corresponding to genes and columns corresponding to anatomical sub-structures. We perform matrix block cluster analysis using a novel row-column mixture model and we relate clustered patterns to Gene Ontology (GO) annotations.
Resulting registrations suggest that our method is robust over intensity levels and shape variations in ISH imagery. Functional enrichment studies from both simple analysis and block clustering indicate that gene relationships consistent with biological knowledge of neuronal gene functions can be extracted from large ISH image databases such as the Allen Brain Atlas  and the Max-Planck Institute  using our method. While we focus here on imagery and experiments of the mouse brain our approach should be applicable to a variety of in situ experiments.