ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks

Yuge Wang, Tianyu Liu, Hongyu Zhao

Abstract: With the advancement of technology, we can generate and access large-scale, high dimensional and diverse genomics data, especially through single-cell RNA sequencing (scRNA-seq). However, integrative downstream analysis from multiple scRNA-seq datasets remains challenging due to batch effects.