TRACER: Twin-contrastive learning for fine-grained and rare spatial domain identification in spatial transcriptomics
Qiaoming Liu, Xin Lu, Bingsen Song, Qianran Wei, Wenzhe Feng, Kexin Guo, Jian Wang, Murong Zhou, Yuming Zhao, Guohua WangAbstract
Motivation
Fine-grained and rare spatial domains are difficult to identify in spatial transcriptomics because they contain few spots, show subtle transcriptional differences, and are strongly influenced by neighboring regions.
Results
We present TRACER, a twin-contrastive learning framework that integrates a graph convolutional autoencoder with neighborhood-aware instance-level and cluster-level contrastive learning. By jointly preserving local spatial continuity and enhancing domain separability, TRACER achieved competitive and generally robust performance compared with representative spatial domain identification methods across multiple spatially resolved transcriptomic datasets, including human dorsolateral prefrontal cortex generated by 10x Visium, mouse brain datasets generated by STARmap, osmFISH, and BARISTAseq, and human breast cancer. TRACER recovered major cortical and anatomical structures, improved the identification of rare or transitional domains in the mouse somatosensory cortex, and revealed candidate functionally distinct small domains within a broadly annotated breast cancer region.
Availability
The source code of TRACER is available at https://github.com/luxin-heart/TRACER
Supplementary information
Supplementary data are available at Bioinformatics online.