Scmaskgan: masked multi-scale CNN and attention-enhanced GAN for scRNA-seq dropout imputation.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, but dropout events, where gene expression is undetected in individual cells, present a significant challenge. We propose scMASKGAN, which transforms matrix imputation into a pixel restoration task to improve the recovery of missing gene expression data. Specifically, we integrate masking, convolutional neural networks (CNNs), attention mechanisms, and residual networks (ResNets) to effectively address dropout events in scRNA-seq data. The masking mechanism ensures the preservation of complete cellular information, while convolution and attention mechanisms are employed to capture both global and local features. Residual networks augment feature representation and effectively mitigate the risk

SUBMITTER: Wu Y 

PROVIDER: S-EPMC12093817 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature