Journal Article

JGR-NMF: joint graph-regularized non-negative matrix factorization for spatial domain identification

Liang, Juan, Wang, Yun, Huang, Jiuxi, Li, Juntao, Xi, Chenxi

2026

Overview

The spatial transcriptomics technique provides an unprecedented perspective for analyzing the distribution patterns of cells within tissues and their functional tissue structures. To enhance the accuracy and robustness of spatial domain identification, we propose Joint Graph-Regularized Non-negative Matrix Factorization (JGR-NMF). An adaptive neighborhood graph construction strategy is introduced by applying an n th-power transformation to the spot adjacency probability matrix, thereby automatically optimizing the neighborhood size for individual spots. Furthermore, a JGR-NMF framework is developed, integrating this adaptively constructed kNN graph with the spatial adjacency matrix. Evaluations conducted on two breast cancer datasets, one Mouse Kidney dataset and one Mouse Embryo dataset, demonstrate that JGR-NMF significantly outperforms five state-of-the-art baseline methods in spatial domain identification. Systematic ablation studies further confirm the critical role of graph regularization in enhancing model performance.

• Publisher: PeerJ. Ltd,PeerJ, Inc,PeerJ Inc
• Subject: Accuracy / Adjacency matrix / Algorithms / Animals / Bioinformatics / Breast cancer / Breast Neoplasms - genetics / Breast Neoplasms - pathology / Cluster analysis / Clustering / Computational Biology / Data analysis / Data mining / Data Mining and Machine Learning / Datasets / Deep learning / Female / Gene expression / Genomics / Health aspects / Humans / Identification / Machine learning / Mathematical models / Methods / Mice / Neighborhoods / Non-negative matrix factorization / RNA sequencing / Spatial transcriptomics / Spatial Transcriptomics - methods / Transcriptomics