A novel statistical method to delineate spatially heterogeneous cell-cell interactions in pancreatic cancer

Music City Center 

The advancement of spatial transcriptomics (ST) technology has revolutionized the ability to profile gene expression while retaining spatial information, offering unprecedented potential to unravel the complexities of cellular function and structure. Cell-cell interactions play a pivotal role in shaping biological processes, and the evolution of ST technologies has led to the development of numerous tools for their analysis. However, existing methods often struggle with accurately quantifying interactions due to the mixture of cell types within tissue samples. Additionally, these methods frequently overlook or encounter challenges in detecting interactions involving rare cell types, which can play crucial roles in disease onset and progression. In this work, we propose a novel method that addresses these limitations by incorporating the complexity of cellular composition into a spatially aware framework for cell-cell interaction quantification, designed specifically for widely used ST platforms such as 10x Visium ST. Our framework leverages information from neighboring spots to robustly quantify both local and global interaction patterns while providing tailored approaches for detecting interactions involving rare cell types. The effectiveness of the proposed method is demonstrated through real data-based simulation studies and its application to spatial transcriptomics datasets from pancreatic cancer patients.