Spatial clustering with random partitions on ovarian cancer data

Spatial transcriptomics (ST) data and single-cell (SC) data provide valuable information for the study of cancer tissues. Our specific goal here is to develop a statistical model and inference approach using both ST and SC data to help the understanding of the tumor micro-environment. For example, whether the boundary between tumor and stromal cells is identifiable? What is the interaction between immune and tumor cells? Some recently proposed method use graph convolutional networks to do spatial clustering, like SpaGCN. Building on SpaGCN as a reference solution, we propose a statistical inference pipeline based on random partition models, to implement uncertainty quantification and joint inference on cell-types and immune profiles.