文献类型：期刊文献

英文题名：Joint Clustering and Analyze Single Cell Multi-omics Data by scMOGAN

作者：An, Congcong[1,2]; Chen, Yiwen[3]; Nie, Shanling[4]; Yang, Hai[1]

机构：[1] East China University of Science and Technology, Department of Computer Science and Engineering, Shanghai, China; [2] Shanghai Key Laboratory of Computer Software Evaluating and Testing, Shanghai, China; [3] National University of Singapore, Center for Continuing and Lifelong Education, Singapore; [4] The University of Sydney, Faculty of Engineering, Australia

年份：2023

起止页码：4862

外文期刊名：Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

收录：EI(收录号：20240715560000)

语种：英文

摘要：Advancements in single-cell multi-omics sequencing technologies have dramatically transformed the analysis of cellular states at single-cell resolution. Cluster analyses leveraging single-cell transcriptome and epigenome data have enabled the characterization of cellular states and the delineation of transcriptomic regulatory programs associated with cellular heterogeneity. However, the high dimensionality, sparsity, and heterogeneity inherent to multi-omics data present significant challenges to their clustering analysis. In this study, we introduced single-cell Multi-Omics Generative Adversarial Networks (scMOGAN), an unsupervised clustering model specifically tailored for single-cell transcriptome and epigenome data. scMOGAN accomplishes this by efficaciously modeling the omics data and mining potential features through neural networks, ultimately utilizing Gaussian mixture models for cell type identification. To validate and scrutinize the performance of scMOGAN, we performed analyses on two actual multi-omics datasets and one simulated dataset. The results demonstrate that scMOGAN surpasses other existing methods in terms of clustering performance, underscoring its efficacy in navigating the complexities of multi-omics data. ? 2023 IEEE.