scGANCL: Bidirectional Generative Adversarial Network for Imputing scRNA-Seq Data With Contrastive Learning

The advent of single-cell RNA sequencing (scRNA-seq) has offering unprecedented insights at the single-cell level. This groundbreaking technology has opened new pathways for understanding cellular diversity and revealing novel insights into disease mechanisms. However, the analysis of scRNA-seq data is challenging, primarily due to dropout events caused by technical noise. Developing effective imputation methods is crucial for the reliable and informative analysis of scRNA-seq data. While deep learning-based approaches have been proposed for scRNA-seq data imputation, they often fall short of optimal performance, especially in identifying rare cell types. Here we propose a novel self-supervised deep learning model named scGANCL for scRNA-seq data imputation. scGANCL combines bidirectional generative adversarial network (BiGAN) with contrastive learning (CL) to enhance imputation performance. To fully exploit gene expression profiles, a contrastive learning module is introduced to enhance the representation learning of cells by minimizing the discrepancy between the distributions of real and generated data. Comprehensive experiments have been conducted on ten simulated and seven real datasets to validate scGANCL's effectiveness. The results demonstrated scGANCL consistently outperformed seven state-of-the-art methods across various downstream tasks. Ablation studies further validated the contribution of each component to the overall performance of the model.