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The recent availability of complete cell lineages from live imaging data opens the way to novel methodologies for the automated analysis of cell dynamics in animal embryogenesis. We propose a method for the calcula- tion of measure-based dissimilarities between cells. These dissimilarity measures allow the use of clustering algorithms for the inference of time-persistent patterns. The method is applied to the digital cell lineages reconstructed from live zebrafish embryos imaged from 6 to 13 hours post fertilization. We show that the position and velocity of cells are sufficient to identify relevant morphological features including bilateral sym- metry and coherent cell domains. The method is flexible enough to readily integrate larger sets of measures opening the way to the automated identification of morphogenetic fields.

Cell Trajectory Clustering: Towards the Automated Identification of Morphogenetic Fields in Animal Embryogenesis