Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq

Machine learning techniques have become increasingly important in analyzing single-cell RNA and identifying cell types, providing valuable insights into cellular development and disease mechanisms. However, the presence of batch effects poses major challenges in scRNA-seq analysis due to data distribution variation across batches. Although several batch effect mitigation algorithms have been proposed, most of them focus only on the correlation of local structure embeddings, ignoring global distribution matching and discriminative feature representation in batch correction. In this paper, we proposed the discriminative domain adaption network (D2AN) for joint batch effects correction and type annotation with single-cell RNA-seq. Specifically, we first captured the global low-dimensional embeddings of samples from the source and target domains by adversarial domain adaption strategy. Second, a contrastive loss is developed to preliminarily align the source domain samples. Moreover, the semantic alignment of class centroids in the source and target domains is achieved for further local alignment. Finally, a self-paced learning mechanism based on inter-domain loss is adopted to gradually select samples with high similarity to the target domain for training, which is used to improve the robustness of the model. Experimental results demonstrated that the proposed method on multiple real datasets outperforms several state-of-the-art methods.