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学术报告(20201024)Deep Joint-Learning Analysis Model of Single Cell Transcriptome and Open Chromatin Accessibility Data
发布时间:2020-10-24

报告题目: Deep Joint-Learning Analysis Model of Single Cell Transcriptome and Open Chromatin Accessibility Data
报告人: 陈洛南 教授
报告人单位: 中国科学院
报告时间: 10月24号(星期六)下午3:30
报告地点: 科技楼北410
报告摘要:

Simultaneous profiling transcriptomic and chromatin accessibility information in the same individual cells offers an unprecedented resolution to understand cell states. However, computationally effective methods for the integration of these inherent sparse and heterogeneous data are lacking. Here, we present a single-cell multimodal variational autoencoder (scMVAE) model, which combines three types of joint-learning strategies with a probabilistic Gaussian Mixture Model (GMM) to learn the joint latent features that accurately represent these multi-layer profiles. Studies on both simulated datasets and real datasets demonstrate that it has more preferable capability (a) dissecting cellular heterogeneity in the joint-learning space, (b) denoising and imputing data, and (c) constructing the association between multi-layer omics data, which can be used for understanding transcriptional regulatory mechanisms.
报告人简介:

Luonan Chen received BS degree in the Electrical Engineering, from Huazhong University of Science and Technology, and the M.E. and Ph.D. degrees in the electrical engineering, from Tohoku University, Sendai, Japan, in 1988 and 1991, respectively. From 1997, he was an associate professor of the Osaka Sangyo University, Osaka, Japan, and then a full Professor. Since 2010, he has been a professor and executive director at Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences. He was elected as the founding president of Computational Systems Biology Society of OR China, and Chair of Technical Committee of Systems Biology at IEEE SMC Society. In recent years, he published over 350 journal papers and two monographs (books) in the area of bioinformatics, nonlinear dynamics and machine learning.


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