Abstract
Multiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, ...inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. Here, we show that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.
Symmetry, which defines invariant properties under a group of transformations, provides a frame of generalization uncovering regularities from given quantitative descriptions. Based on the Curie’s ...symmetry principle, connecting between causality and symmetry, we formulate the intuitive but formal selection rules and apply to determine the excitable resonant modes of a photonic crystal defect cavity, which is an important element for plasmonic applications. Quantitative agreement with the numerical simulations demonstrates the effectiveness of the fundamental principle in finding the critical symmetry conditions for the available localized defect states within photonic crystals. Moreover, the principle facilitates analysis of the higher-order or even forbidden modes in the asymmetric excitation configurations regarding the polarizations or positions of the light source, which typically require heavy computations. Our results may be extended similarly to develop the qualitative selection rules in other physical systems with a geometric symmetry.
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