Abstract:

    Spatial transcriptome is informative to infer cell cell communications. Existing methods focus on inference of cell communications at cell type level, but do not identify individual cells involved in the communications. We propose a graphical modeling framework, that takes spatial transcriptome as input, and infers cell-cell communication network at single cell resolution. Our method builds upon the heuristic that physically adjacent cells are more likely to communicate with each other. We have demonstrated in simulations and real datasets that our approach outperforms existing methods in accuracy, and improves sensitivity to identify interactions involving rare cell types. We have applied the proposed method to a dataset concerning mouse brain, and revealed communication hot spots, thus providing additional insight of the underlying biology.