2-AIN-506, 2-AIN-252: Seminar in Bioinformatics (2), (4)
Summer 2025
Abstrakt

You Wu, Li Xu, Xiaohong Cong, Hanxiao Li, Yanli Li. Scmaskgan: masked multi-scale CNN and attention-enhanced GAN for scRNA-seq dropout imputation. BMC bioinformatics, 26(1):130. 2025.

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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 of model overfitting. Additionally, cell-type labels are 
incorporated as constraints to guide the methods in learning more accurate 
cellular features. Finally, multiple experiments were conducted to evaluate the 
methods' performance using seven different data types and scRNA-seq data from ten 
neuroblastoma samples. The results demonstrate that the data imputed by scMASKGAN 
not only perform excellently across various evaluation metrics but also 
significantly enhance the effectiveness of downstream analyses, enabling a more 
comprehensive exploration of underlying biological information.