Single-cell RNA sequencing technology facilitates the recognition of diverse cell types and subgroups, playing a crucial role in investigating cellular heterogeneity. Cell type annotation, a crucial process in single-cell RNA sequencing analysis, is often influenced by noise and batch effects. To address these challenges, we propose scRDAN, which is a robust domain adaptation network comprising three modules: the denoising domain adaptation module, the fine-grained discrimination module, and the robustness enhancement module. The denoising domain adaptation module mitigates noise interference through feature reconstruction in domains, while leveraging adversarial learning to align data distributions, improving annotation accuracy and robustness against batch effects. The fine-grained discrimination module maintains intra-class compactness and enhances inter-class separability, reducing feature overlap and improving cell type distinction. Finally, the robustness enhancement module introduces noise from various perspectives in both domains, enhancing robustness and generalization. We evaluate scRDAN on simulated, cross-platforms, and cross-species datasets, comparing it with advanced methods. Results demonstrate that scRDAN outperforms existing methods in handling batch effects and cell type annotation.

Cell type annotation refers to the process of categorizing and labeling cells to identify their specific cell types, which is crucial for understanding cell functions and biological processes. Although many methods have been developed for automated cell type annotation, they often encounter challenges such as batch effects due to variations in data distribution across platforms and species, thereby compromising their performance. To address batch effects, in this study, a pre-trained domain adaptation model based on structural similarity, named pscAdapt, is proposed for cell type annotation. Specifically, a pre-trained strategy is employed to initialize model parameters to learn the data distribution of source domain. This strategy is also combined with an adversarial learning strategy to train the domain adaptation network for achieving domain level alignment and reducing domain discrepancy. Furthermore, to better distinguish different types of cells, a structural similarity loss is designed, aiming to shorten distances between cells of the same type and increase distances between cells of different types in feature space, thus achieving cell level alignment and enhancing the discriminability of cell types. Comprehensive experiments were conducted on simulated datasets, cross-platforms datasets and cross-species datasets to validate the effectiveness of pscAdapt, results of which demonstrate that pscAdapt outperforms several popular cell type annotation methods.