Identifying spatial domains is critical for understanding breast cancer tissue heterogeneity and providing insights into tumor progression. However, dropout events introduces computational challenges and the lack of transparency in methods such as graph neural networks limits their interpretability. This study aimed to decipher disease progression-related spatial domains in breast cancer spatial transcriptomics by developing the three graph regularized non-negative matrix factorization (TGR-NMF). A unitization strategy was proposed to mitigate the impact of dropout events on the computational process, enabling utilization of the complete gene expression count data. By integrating one gene expression neighbor topology and two spatial position neighbor topologies, TGR-NMF was developed for constructing an interpretable low-dimensional representation of spatial transcriptomic data. The progressive lesion area that can reveal the progression of breast cancer was uncovered through heterogeneity analysis. Moreover, several related pathogenic genes and signal pathways on this area were identified by using gene enrichment and cell communication analysis.
Keywords: breast cancer; non-negative matrix factorization; spatial domain; spatial transcriptomics.
© The Author(s) 2025. Published by Oxford University Press.