GALA: Integrating Weighted Graph Walks and Latent-Space Adversarial Training for Single-Cell Batch Alignment

Single-cell RNA sequencing (scRNA-seq) enables unprecedented exploration of cellular heterogeneity, yet technical variations across datasets introduce pervasive batch effects that severely compromise integrative analysis. We present Graph-based Adversarial Latent Alignment (GALA), a novel batch correction framework that synergistically integrates weighted graph random walks with latent space adversarial training to robustly align scRNA-seq data while preserving critical biological signals. GALA employs a Weighted Graph Mutual Nearest Neighbor (WGMNN) module that achieves up to 125% improvement in cross-batch cell pairing diversity and 48% increase in coverage compared to conventional approaches, substantially enhancing the detection of biologically meaningful correspondences between batches. These optimized cell pairs guide adversarial training within a carefully designed low-dimensional latent space, generating batch-agnostic representations that simultaneously eliminate technical artifacts while faithfully preserving biological variability. Evaluated across five benchmark datasets representing diverse real-world scenarios-including identical cell types, non-identical cell types, and complex multi-batch settings-GALA demonstrates robust performance in batch correction and biological signal preservation, achieving high F1 scores of 0.91, 0.82, 0.88, 0.82, and 0.76, respectively. GALA outperforms established methods including Seurat v4, Harmony, and Scanorama, with notable advantages in challenging datasets with heterogeneous cell populations. Aggregating performance across all datasets, GALA achieves the highest overall integration score and best average rank while maintaining high computational efficiency, demonstrating consistent superiority and strong robustness across diverse scRNA-seq integration scenarios.