Table1_RECCIPE: A new framework assessing localized cell-cell interaction on gene expression in multicellular ST data.XLSX

Cell-cell interaction (CCI) plays a pivotal role in cellular communication within the tissue microenvironment. The recent development of spatial transcriptomics (ST) technology and associated data analysis methods has empowered researchers to systematically investigate CCI. However, existing methods are tailored to single-cell resolution datasets, whereas the majority of ST platforms lack such resolution. Additionally, the detection of CCI through association screening based on ST data, which has complicated dependence structure, necessitates proper control of false discovery rates due to the multiple hypothesis testing issue in high dimensional spaces. To address these challenges, we introduce RECCIPE, a novel method designed for identifying cell signaling interactions across multiple cell types in spatial transcriptomic data. RECCIPE integrates gene expression data, spatial information and cell type composition in a multivariate regression framework, enabling genome-wide screening for changes in gene expression levels attributed to CCIs. We show that RECCIPE not only achieves high accuracy in simulated datasets but also provides new biological insights from real data obtained from a mouse model of Alzheimer’s disease (AD). Overall, our framework provides a useful tool for studying impact of cell-cell interactions on gene expression in multicellular systems.细胞间相互作用（cell-cell interaction, CCI）在组织微环境的细胞通讯中发挥关键作用。近年来，空间转录组学（spatial transcriptomics, ST）技术及其配套数据分析方法的发展，使得研究者能够系统性地探究细胞间相互作用。然而，现有方法多针对单细胞分辨率数据集开发，但绝大多数空间转录组平台并不具备该分辨率。此外，基于依赖结构复杂的空间转录组数据通过关联筛选来检测细胞间相互作用时，由于高维空间中存在多重假设检验问题，需对错误发现率进行合理控制。为解决上述挑战，本文提出RECCIPE——一种用于在空间转录组数据中识别多种细胞类型间细胞信号相互作用的全新方法。RECCIPE在多变量回归框架中整合基因表达数据、空间信息与细胞类型组成，可实现全基因组范围内对细胞间相互作用所导致的基因表达水平变化的筛选。实验结果表明，RECCIPE不仅在模拟数据集中表现出较高的准确性，还能从阿尔茨海默病（Alzheimer’s disease, AD）小鼠模型的真实数据中挖掘出新的生物学见解。总体而言，本研究提出的框架为探究多细胞系统中细胞间相互作用对基因表达的影响提供了实用工具。