The Processing and Analysis of in situ Gene Expression Images of the Mouse Brain
Manjunatha N. Jagalur1, Chris Pal1*, Erik Learned-Miller1, R. T. Zoeller2 and David Kulp1
1Department of Computer Science,
2Department of Biology & The Laboratory of Molecular and Cellular Neurobiology
*Contact author: email@example.com
Many important high throughput projects are now underway which 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 atlases for the embryonic fruit fly, embryonic and adult mouse at an unprecedented genomic scale already involve the analysis of hundreds of thousands of high resolution experimental images. We present an end-to-end approach for processing raw images and performing analysis. We use a non-linear image registration technique specifically adapted for mapping expression images to anatomical annotations and a method for extracting expression information. 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 more broadly applicable to other in situ experiments but we focus our analysis here on imagery and experiments of the mouse brain – an application with tremendous potential for increasing our fundamental understanding of neural information processing systems.
Image registration, Mutual Information, Bi-clustering, in situ gene expression, high throughput, bioinformatics.
Manjunatha N. Jagalur, Chris Pal, Erik Learned-Miller, R. T. Zoeller and
David Kulp. (2007) Analyzing in situ Gene Expression in the
Mouse Brain with Image Registration, Feature Extraction and Block Clustering
BMC Bioinformatics vol. 8, suppl. 10, Dec. 21.
Manjunatha N. Jagalur, Chris Pal, Erik Learned-Miller, R. T. Zoeller and David Kulp. (2006) The Processing and Analysis of in situ Gene Expression Images of the Mouse Brain. In Advances in Neural Information Processing Systems (NIPS) Workshop on New Problems and Methods in Computational Biology.
Paper in PDF format, here (Note: File is 25 MB).
Step 1: Image Registration
Red channel is the expression image, green channel is the reference image. (Left) Before registration. (Middle) After approximate registration. (Right) After our adaptive
non-linear registration step anatomical patterns are extracted using a mask (shown in blue).
Step 2: Analysis via row column clustering.