Document Type
Article
Publication Date
2013
DOI
10.1186/1471-2105-14-372
Publication Title
BMC Bioinformatics
Volume
14
Pages
1-10
Abstract
Background: Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.
Results: We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.
Conclusions: Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods.
Original Publication Citation
Zhang, W. L., Feng, D. M., Li, R. J., Chernikov, A., Chrisochoides, N., Osgood, C., . . . Ji, S. W. (2013). A mesh generation and machine learning framework for Drosophila gene expression pattern image analysis. BMC Bioinformatics, 14, 1-10. doi: 10.1186/1471-2105-14-372
Repository Citation
Zhang, W. L., Feng, D. M., Li, R. J., Chernikov, A., Chrisochoides, N., Osgood, C., . . . Ji, S. W. (2013). A mesh generation and machine learning framework for Drosophila gene expression pattern image analysis. BMC Bioinformatics, 14, 1-10. doi: 10.1186/1471-2105-14-372