Abstract <p>The latest progress in spatial transcriptomics has empowered scientists to investigate spatial heterogeneity with single-cell precision. A pivotal yet demanding aspect of spatial transcriptomics data analysis is cell type annotation. However, current methods exhibit limited performance as they are primarily designed for scRNA-seq data. Especially, these approaches often neglect spatial coordinate information and encounter challenges in identifying novel cell types. Here, we introduce SANNO, a novel approach that employs Optimal Transport (OT) to concurrently identify both known and novel cell types in spatially resolved single-cell data. Specifically, SANNO leverages a graph-Transformer module to model spatial coordinates and gene expression. This produces unified representations for both reference and query data. Building on this, SANNO employs a dual-strategy classifier. The first is an Unbalanced Optimal Transport (UOT) module that aligns query data with reference prototypes. The second is a self-supervised OT-based module that enhances global cluster separation and local cellular consistency, effectively eliminating batch effects. To further improve prediction accuracy, SANNO integrates an entropy-based re-weighted loss function. This significantly boosts the confidence of query cell predictions. Comprehensive experiments reveal that SANNO surpasses state-of-the-art techniques across both intra- and cross-spatial datasets, particularly in the identification of novel cell types. Additionally, SANNO demonstrates commendable performance in annotating cells within single-cell data, underscoring its potential as a versatile tool for cell annotation across single-cell and spatial transcriptomics datasets.</p> Graphical Abstract <p></p>