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0The paper introduce SQUINT, a graph vector-quantized variational autoencoder designed to process spatially-resolved transcriptomics data by simultaneously learning distinct cell and niche codebooks. This framework utilizes residual vector quantization to capture both cell-intrinsic identities and broader microenvironmental contexts in a hierarchical discrete token format. By incorporating domain-specific training objectives such as adjacency, contrastive, and batch-correction losses, the model successfully merges spatial structural information across multiple tissue sections and assays. Extensive benchmarking across diverse datasets demonstrates that SQUINT either outperforms or remains competitive with leading continuous representation and single-cell foundation models on tasks ranging from cellular identification to cross-section integration. Furthermore, the resulting discrete vocabulary facilitates advanced biological applications, including gene-expression imputation in hidden regions and streamlined query-to-reference mapping for complex tissue atlases.
References:
Birk S, Merchant A, Vahidi A, et al. Learning Discrete Cell and Niche Codes from Spatial Transcriptomics Using Dual Residual Vector Quantization[J]. bioRxiv, 2026: 2026.08. 07.743490.
