Background: Multicellular organisms consist of cells of many different types that are established during\r\ndevelopment. Each type of cell is characterized by the unique combination of expressed gene products as a result of\r\nspatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene\r\nexpression controls that generate the complex body plans during development. Recent advances in high-throughput\r\nbiotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism\r\nfruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on\r\ncomputational tools we present in this paper would provide promising ways for addressing key scientific questions.\r\nResults: We develop a set of computational methods and open source tools for identifying co-expressed embryonic\r\ndomains and the associated genes simultaneously. To map the expression patterns of many genes into the same\r\ncoordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform\r\na meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes\r\nand the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes\r\nsimultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key\r\ndevelopmental events during the stages of embryogenesis we study. The open source software tool has been made\r\navailable at http://compbio.cs.odu.edu/fly/.\r\nConclusions: Our mesh generation and machine learning methods and tools improve upon the flexibility,\r\nease-of-use and accuracy of existing methods
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