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Life Webinar | Bioinformatics Methods for Single Cell Sequencing Data Analysis

9 Feb 2022, 01:00 (CET)

Single Cell, Omics Data, Bioinformatics, Clustering, Cell Type Annotation, Regulatory Network, Cellcell Communication, Cell Evolution
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Welcome from the Chair

1st Life Webinar

Bioinformatics Methods for Single Cell Sequencing Data Analysis

As an emerging biotechnology, single cell sequencing has become widely used. However, single cell data analysis is still challenging. Compared with traditional bulk sequencing, single cell sequencing data are sparse. There are many genes in cells that cannot be measured. Another difference is that the sample size of single cell data is usually much larger than that of bulk sequencing data, enabling the applications of the latest deep learning methods which require large sample sizes. Furthermore, the QC (quality control) of single cell sequencing data is different from that of bulk sequencing. There are many new issues. For example, a cell with a unique barcode may be a doublet, which leads to different data processing methods.

To address these challenges in single cell sequencing data analysis, new bioinformatics methods are needed. Renowned scientists of the area will present novel bioinformatics methods for single cell sequencing data analysis in this webinar.

The related Special Issue "Bioinformatics Methods for Single Cell Sequencing Data Analysis", which will be published by the open access journal Life, is open for submissions. More details can be found here.

Date: 9 February 2022

Time: 1:00am CET | 8:00am CST Asia | 7:00pm EST (8 Feb 2022)

Webinar ID: 876 5518 8329

Webinar Secretariat:


Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, China

Tao Huang is an Associate Professor at Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences. He completed his post-doctoral research at Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York City, USA. His research interests include bioinformatics, computational biology, systems genetics, and big data research. He has published over 200 articles. His works have been cited 10,219 times with an h-index of 47. He has edited books of Computational Systems Biology: Methods and Protocols and Precision Medicine for Methods in Molecular Biology series. He has been an Editor or Guest Editor for over 30 journals and Reviewer for 170 journals. He has been awarded the titles of Highly Cited Chinese Researcher (2020) and World's Top 2% Scientist (2020, 2021).

Invited Speakers

Institute of the Biology of Aging, University of Minnesota, USA

Dr. Dong obtained his PhD in bioinformatics in the Shanghai Institute of Biological Sciences at the Chinese Academy of Sciences in 2013. He completed postdoc training in the Department of Genetics at the Albert Einstein College of Medicine in 2021. Since 2021, he has started the Dong laboratory in the Institute of the Biology of Aging (iBAM) and the Department of Genetics, Cell Biology, and Development (GCD) at the University of Minnesota, Twin Cities. The general interest of the Dong Laboratory is discovering causal mechanisms of human aging. Currently, it focuses on testing the mutation theory of aging: if accumulation of DNA mutations in normal somatic cells is a causal mechanism to age-related functional decline. The lab approaches this by developing and applying state-of-the-art single-cell multi-omics technologies and machine learning algorithms.

School of Basic Medical Sciences, Xi'an Jiaotong University, China

Yungang Xu, PhD, Professor of Xi’an Jiaotong University, China. He earned his Ph.D. from the School of Computer Science and Technology at the Institute of Technology (HIT), China. He also holds Bachelor's and Master's degrees in Biology. Between January 2015 and February 2021, he moved to the U.S.A. and worked as a Postdoctoral Research Fellow and Research Assistant Professor at Wake Forest University, UT Health, and the Children’s Hospital of Philadelphia and University of Pennsylvania, successively. His research interest covers bioinformatics, machine learning, epigenetics and transcriptional regulation, and single-cell omics. He has published more than 20 papers in journals of his field, such as Genome Biology, AJHG, Nucleic Acids Research, Briefings in Bioinformatics, and Bioinformatics. Specifically, he dedicates himself to translating big genomic data, epigenomic data, and transcriptomic data into scientific insights and clinical knowledge. He has been awarded one provincial and one departmental science and technology award from the Chinese government. He hosted one general project of the National Natural Science Foundation of China (NSFC) and participated in four NSFC projects as well as four NIH projects as the core member. He is the editor of multiple journals and was the committee member of multiple international conferences.

Changping Laboratory, Beijing, China

Dr. Xianwen Ren’s research interest focuses on developing effective mathematical models and algorithms to solve biomedical questions based on omics data. He graduated in Nankai University in 2004 and got double bachelor degrees in biology and mathematics. In 2010, he graduated from the Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences and received his PhD degree in operation research and cybernetics. Then, he joined Institute of Pathogen Biology, Chinese Academy of Medical Sciences as an assistant and associate professor, where he focused on developing algorithms for accurate and effective diagnosis of viral infection based on metagenomics sequencing. His research achievements were awarded the 1st class Prize of Scientific and Technological Progress by Ministry of Education, China. In 2016, he joined BIOPIC, Peking University and has focused on developing mathematical models and algorithms to use scRNA-seq data answer tumor immunological questions. He has published more than 30 papers as (co-)corresponding and (co-)first authors in Cell, Nature, Nature Medicine, Nature Communications, Genome Biology, Nucleic Acids Research, etc. He is also an associate editor of Frontiers in Genetics and serves as reviewers for multiple bioinformatic journals.

Webinar Content

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Time in CST Asia
(9 Feb 2021)

Time in EST
(8 Feb 2021)

Prof. Dr. Tao Huang

Chair Introduction

8:00 – 8:10 am

7:00 – 7:10 pm

Prof. Dr. Xiao Dong

Discovering Somatic Mutations in Single Cells during Aging

8:10 – 8:30 am

7:10 – 7:30 pm

Prof. Dr. Yungang Xu

Generative Adversarial Networks for Single-Cell RNA-Seq Imputation

8:30 – 8:50 am

7:30 – 7:50 pm

Prof. Dr. Xianwen Ren

Spatial Reconstruction of Single-Cell RNA-Seq Data: A de novo Approach

8:50 – 9:10 am

7:50 – 8:10 pm

Prof. Dr. Tao Huang

Cell Marker Identification with Machine Learning Methods

9:10 – 9:30 am

8:10 – 8:30 pm

Q&A Session

9:30 – 9:50 am

8:30 – 8:50 pm

Closing of Webinar
Prof. Dr. Tao Huang

9:50 – 10:00 am

8:50 – 9:00 pm

Relevant SI

Bioinformatics Methods for Single Cell Sequencing Data Analysis
Guest Editors: Prof. Dr. Yudong Cai & Dr. Tao Huang
Deadline for manuscript submissions: 15 June 2022

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