Summary
To make possible a more rigorous understanding of animal gene regulatory networks, the Berkeley Drosophila Transcription Network Project (BDTNP) has developed a suite of methods that support quantitative, computational analysis of three-dimensional (3D) gene expression patterns with cellular resolution in early Drosophila embryos.
Defining the pattern of gene expression is an essential step toward further analysis in order to derive knowledge about the characteristics of gene expression patterns and to identify and model gene inter-relationships. To address this challenging task we have developed an integrated, interactive approach toward pattern segmentation. Here, we introduce a ridge-detection-based 3D gene expression pattern segmentation algorithm. We compare this algorithm to common 2D pattern segmentation methods, such as thresholding and edged-detection-based methods, which we have adapted to 3D pattern segmentation. We show that such automatic strategies can be improved to obtain better segmentation results by user interaction and additional post-processing steps.
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Huang, MY. et al. (2008). Segmenting Gene Expression Patterns of Early-stage Drosophila Embryos. In: Linsen, L., Hagen, H., Hamann, B. (eds) Visualization in Medicine and Life Sciences. Mathematics and Visualization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72630-2_18
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DOI: https://doi.org/10.1007/978-3-540-72630-2_18
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