@article{Lan2025, 
author = {Wei Lan and Guo-Hang He and Wei-Hao Zhou and Qing-Feng Chen and Rui-Qing Zheng and Yi Pan and Yi-Ping Phoebe Chen},
title = {scMCG: Analyzing a Single-Cell Assay for Transposase-Accessible Chromatin Using Sequencing Data Based on Contrastive Learning and Generative Adversarial Network},
year = {2025},
journal = {Journal of Computer Science and Technology},
volume = {40},
number = {6},
pages = {1639-1649},
keywords = {single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq), contrastive learning, convolutional neural network, generative adversarial network},
url = {https://www.sciopen.com/article/10.1007/s11390-025-4969-z},
doi = {10.1007/s11390-025-4969-z},
abstract = {The development of single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) has significantly advanced the study of cell heterogeneity in the epigenetic landscape. Numerous studies have leveraged scATAC-seq data to explore deeper gene regulatory relationships. However, scATAC-seq usually faces dropout events which may result in data sparsity and noise. In this work, we propose a method (scMCG) for analyzing scATAC-seq data that employs contrastive learning and a generative adversarial network (GAN). First, the scMCG method uses two distinct encoders for contrastive learning to solve the issues of feature redundancy and data sparsity in scATAC-seq data. Subsequently, a generator is used to reconstruct the latent embedding. Finally, a decoder is used to generate binary accessibility. We conduct experiments on multiple scATAC-seq datasets. The results demonstrate that the scMCG method achieves excellent performance in multiple tasks such as cell clustering and transcription factor activity influence.}
}