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Muhammed Khawatmi, Shivprasad Jamdade, and Heba Sailem (King's College London)
Visualization of spatial datasets is essential for understanding biological systems that are composed of several interacting cell types and scales. Representing such high dimensional data effectively to facilitate data interpretation is a major challenge. Existing methods such as spatially plotted pie or dot charts obscure underlying tissue regions and necessitate switching between different views for accurate interpretations. Here, we present TissuePlot, a novel method for visualizing spatial data at molecular, cellular and tissue levels in the context of their spatial locations. To this end, TissuePlot employs a transparent hexagon tessellation approach that utilizes object borders to represent cell composition or gene-level data without obscuring the underlying tissue image. Moreover, it offers a multi-view interactive web app with an intuitive design (https://sailem-group.github.io/TissuePlot/). Several demos demonstrate how TissuePlot allows interrogating spatial tissue data at multiple scales, linking molecular information to tissue anatomy and motifs.