PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications

Jiarui Feng, Haoran Song, Michael Province, Guangfu Li, Philip R.O. Payne, Yixin Chen, Fuhai Li

Research output: Contribution to journalArticlepeer-review

Abstract

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 scRNA-seq data. In this study, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on the divide-and-conquer strategy. This model divides complex signaling networks into signaling paths, which are then scored and ranked using a novel graph transformer architecture to infer 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 promising potential of using PathFinder as a general signaling network inference model.

Original languageEnglish (US)
Article number1369242
JournalFrontiers in Cellular Neuroscience
Volume18
DOIs
StatePublished - 2024

Keywords

  • Alzheimer’s disease
  • cell cell signaling communications
  • graph neural network
  • microenvironment
  • signaling pathways

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience

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