Non-random spatial patterns in biological phenomena are solid indicators for collective behavior. Tissue morphology and, ultimately, tissue function are culminations of collective cell behaviors. This research presents several novel spatial statistics designed explicitly for use in microanatomical analyses to characterize better the spatial patterns of cells, the mechanisms that create them, and their influence on cell regulation and tissue function. Spatial statistics encompass numerous formal methods that seek to study entities based upon their distribution, orientation, or geometric attributes. Though widely used to study large-scale spatial properties, many traditional spatial statistics are poorly suited for application in microanatomy. We developed an intuitive method to procedurally generate spatial patterns likely to be present in histological specimens to address this. By applying this method, we identified the major weaknesses of pre-existing spatial statistics. With these limitations in mind, we adapted several spatial statistics for use in histological analyses. We demonstrated the utility of these improved statistics by identifying correlations between stem cell dynamics and the spatial distribution of crucial regulatory cells in the bone marrow. Next, we developed advanced metrics for cell alignment and polarity by synergizing spatial statistics and morphological operations. Leveraging these metrics we confirmed apolarity and misalignment in a specialized subpopulation of cells in a sensory neuroepithelia in the inner ear. These findings ruled out a possible explanation for discrepancies between vertebrate and invertebrate tissue development. Altogether, the tools presented here improve the accessibility and flexibility of spatial statistics, permitting more robust analyses of spatial patterning and morphology in tissues.
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