Abstract
Single-cell RNA sequencing (scRNA-seq) technology has opened new frontiers in biomedical research, offering insights into cellular heterogeneity. Accurate cell clustering and batch effect correction are essential in single-cell RNA sequencing (scRNA-seq) data analysis, forming the foundation for downstream steps. However, most methods handle these tasks separately, limiting their applicability across diverse datasets. To address these challenges, we introduce Relational Graph Convolutional Network with Batch Awareness (RGCN-BA), a deep learning framework that integrates cell clustering and batch effect correction into a unified model. For multi-batch datasets, RGCN-BA leverages relational graph convolutional network to process batch information as distinct edge types, followed by a batch correction layer for global alignment. For single-batch data, it functions with a single edge type. Experiments on both multi-batch and single-batch datasets demonstrate that RGCN-BA outperforms both specialized clustering methods and batch effect correction methods. This versatility in handling both tasks positions RGCN-BA as a powerful tool for enhancing scRNA-seq data analysis.