Description

With the advances of sequencing techniques, single cell sequencing opens an unprecedented opportunity for multiomics analysis at single-cell resolution. It has allowed us to simultaneously profile multiomic layers of DNA, RNA, protein, methylation, or open chromatin nucleosome positioning in single cell to analyze the underlying mechanisms of human diseases and cellular development. To date, many algorithms have been designed to analyze different types of single cell data, and a majority of them have achieved great success in dealing with problems such as sequencing artifacts, grouping cells into cell types, and inferring cell trajectory. However, most of the approaches are data-dependent and have their own pros and cons in dealing with different experimental design and data types. Development of algorithms and tools for integrative analysis of multiomic single-cell profiling is a crucial step toward understanding the interplay of biological systems.

We invite investigators to contribute Original Research on computational methods in analyzing single cell multiomic data, and their applications in molecular biological and complex diseases. Potential topics include but are not limited to the following:

Computational models for integrating multiomic single-cell profiling, e.g. scRNA-seq, scATAC-seq, scCLIP-seq, single-cell spatial transcriptomics.
Novel analyses to identify risk factors of cancers or complex genetic diseases in single-cell resolution
Bioinformatics software and databases for analyzing single-cell data.
Imputation and cell type clustering on different types of single-cell data
New algorithms or tools to analyze copy number variation on scRNA-seq data
Trajectory inference
Computational tools to generate synthetic single-cell data
New methods to reconstruct gene regulatory network
Algorithms to integrate single-cell and bulk sequencing data

Important Dates (Tentative)

Oct. 20, 2021: Due date for full workshop papers submission
Nov. 5, 2021: Notification of paper acceptance to authors
Nov. 21, 2021: Camera-ready of accepted papers
Dec. 11, 2021: Workshops

Program Chairs

Eric Lu Zhang, Department of Computer Science, Hong Kong Baptist University
Xin (Maizie) Zhou, Department of Biomedical Engineering and Computer Science, Vanderbilt University
Xian Fan Mallory, Department of Computer Science, Florida State University
Debajyoti Chowdhury, School of Chinese Medicine, Hong Kong Baptist University

Program Committee Members

Ruli Gao, Houston Methodist Hospital
Wenji Ma, Chinese Academy of Science
Yong Fei Wang, The University of Hong Kong
Wenjun Shen, Shantou University
Zheng Hu, University of Chinese Academy of Sciences
Ye Yuan, Shanghai Jiao Tong University

KEYNOTE SPEAKERS

Wei Vivian Li，Assistant Professor, Department of Biostatistics and Epidemiology， Rutgers School of Public Health, Rutgers university
Ye Yuan, Associate Professor , Department of Automation, Shanghai Jiao Tong University

Workshop Submission Requirement

Please submit a full-length paper (up to 8 page IEEE 2-column format ) through the online submission system (you can download the format instruction here. Electronic submissions (in PDF or Postscript format) are required. Selected participants will be asked to submit their revised papers in a format to be specified at the time of acceptance.

Workshop Online Submission:

Submit a paper Click here

Time Table

Time	Title	Presenter/Author
40 mins	Gene relationship inference from multimodality single cell expression data	Ye Yuan (Keynote)
20 mins	(B461) Time-Series Analysis of Gene Correlation Networks based on Single-cell Transcriptome Data	Yasuhito Asano
20 mins	(B501) The effect of the infarct regions on vulnerability to reentry in two different stages of myocardial infarction	Cuiping Liang
20 mins	(B527) Integration of Multiple scRNA-Seq Datasets on the Autoencoder Latent Space	Andrea Tangherloni
	Coffee Break
40 mins	Model-based Analysis of Alternative Polyadenylation Using 3’ End Reads	Wei Li (Keynote)
20 mins	(B725) A Bayesian framework for inferring heterogeneity of cellular processes using single-cell data	Tianhai Tian
20 mins	(B816) Single-cell RNA sequencing data clustering using graph convolutional networks	Sheida Nabavi
	Closing Remarks