Authors: Jiacheng Liu, Xingyu Fan, Chunbin Gu, Yaodong Yang, Bian Wu, Guangyong Chen, Chang‐Yu Hsieh, Pheng‐Ann Heng
Published: 2025-03-10
Source: Full article
AbstractSingle‐cell RNA sequencing (scRNA‐seq) has unveiled extensive cellular heterogeneity, yet precise cell type annotation and the identification of novel cell populations remain significant challenges. scHeteroNet, a novel graph neural network framework specifically designed to leverage heterophily in scRNA‐seq data, is presented. Unlike traditional methods that assume homophily, scHeteroNet captures complex cell–cell interactions by integrating information from both immediate and extended cellular neighborhoods, resulting in highly accurate cell representations. Additionally, scHeteroNet incorporates an innovative novelty propagation mechanism that robustly detects previously uncharacterized cell types. Comprehensive evaluations across diverse scRNA‐seq datasets demonstrate that scHeteroNet consistently outperforms state‐of‐the‐art approaches in both cell type classification and novel cell detection. This heterophily‐aware approach enhances the ability to uncover cellular diversity, providing deeper insights into complex biological systems and advancing the field of single‐cell analysis.