PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications - Feng, Jiarui; Song, Haoran; Province, Michael; Li, Guangfu; Payne, Philip R. O.; Chen, Yixin; Li, Fuhai (Frontiers in Cellular Neuroscience, 2024)
Recently, large-scale scRNA-seq datasets have been generated to understand the complex signaling mechanisms within the microenvironment of Alzheimer’s Disease (AD), which are critical for identifying novel therapeutic targets and precision medicine. However, the background signaling networks are highly complex and interactive. It remains challenging to infer the core intra- and inter-multi-cell signaling communication networks using the scRNA-seq data. Herein, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and signaling communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on the divide-and-conquer strategy, which divides the complex signaling networks into signaling paths, and then scores and ranks them using a novel graph transformer architecture to infer the intra- and inter-cell signaling communications. We evaluated the performance of PathFinder using two scRNA-seq data cohorts. The first cohort is an APOE4 genotype-specific AD and the second is a human cirrhosis cohort. The evaluation confirms the potential of using PathFinder as a general signaling network inference model.