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Activity Number: 331 - Recent Developments in High-Throughput, Large-Scale Biomedical Data Analysis
Type: Invited
Date/Time: Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
Sponsor: International Chinese Statistical Association
Abstract #314429
Title: Statistical Analysis of Multi-Sample, Single-Cell RNA-Seq Data with Applications to COVID-19
Author(s): Hongkai Ji* and Zhicheng Ji and Boyang Zhang and Runzhe Li and Weiqiang Zhou and Wenpin Hou and Zhao Ruzhang and Wang Yi and Zeyu Chen
Companies: Johns Hopkins Bloomberg School of Public Health and Duke University School of Medicine and Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health and Institute of Immunology, University of Pennsylvania
Keywords: single cell; RNA-seq; COVID-19; genomics; data integration; modeling
Abstract:

As single-cell RNA-seq (scRNA-seq) is increasingly used in biomedical research, scRNA-seq datasets with multiple patient samples become common. In this talk, I will discuss emerging issues in the analysis of multi-sample scRNA-seq data. I will present solutions to identifying differential genes, constructing pseudotemporal trajectories using multiple scRNA-seq samples, characterizing cross-sample variation and linking it to sample phenotype. The methods will be demonstrated via an integrative analysis of COVID-19 single-cell RNA-seq data.


Authors who are presenting talks have a * after their name.

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