Abstract:
<p>Development of a single cell into an adult organism is accomplished through an elaborate and complex cascade of spatiotemporal gene expression. While methods exist for capturing spatiotemporal expression patterns---in situ hybridization, reporter constructs, fluorescent tags---these methods have been highly laborious, and results are frequently assessed by subjective qualitative comparisons. To address these issues, methods must be developed for automating the capture of images, as well as for the normalization and quantification of the resulting data. In this thesis, I design computational approaches for high throughput image analysis which can be grouped into three main areas. First, I develop methods for the capture of high resolution images from high throughput platforms. In addition to the informatics aspect of this problem, I also devise a novel multiscale probabilistic model that allows us to identify and segment objects in an automated fashion. Second, high resolution images must be registered and normalized to a common frame of reference for cross image comparisons. To address these issues, I implement approaches for image registration using statistical shape models and non-rigid registration. Lastly, I validate the spatial expression data obtained from microscopy images to other known spatial expression methods, and develop methods for comparing and calculating the significance between spatial expression patterns. I demonstrate these methods on two model developmental organisms: Arabidopsis and Drosophila.</p>
