Many important high throughput projects use in situ gene expression detection technology and require the analysis of images of spatial cross sections of organisms taken at 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. We present an end-to-end approach for processing raw in situ expression imagery and performing subsequent analysis. We use a nonlinear image registration technique specifically adapted for mapping expression images to anatomical annotations and a method for extracting expression information within an anatomical region. We also present a new approach for jointly clustering the rows and columns of a matrix and we relate clustered patterns to Gene Ontology (GO) annotations. Our approach should be applicable to a variety of in situ experiments but we focus here on imagery and experiments of the mouse brain – an application with tremendous potential for increasing our fundamental understanding of neural information processing systems.