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Activity Number:
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331
- Recent Developments in High-Throughput, Large-Scale Biomedical Data Analysis
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Type:
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Invited
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Date/Time:
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Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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International Chinese Statistical Association
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Abstract #314429
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Title:
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Statistical Analysis of Multi-Sample, Single-Cell RNA-Seq Data with Applications to COVID-19
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Author(s):
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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
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Companies:
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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
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Keywords:
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single cell;
RNA-seq;
COVID-19;
genomics;
data integration;
modeling
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Abstract:
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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.
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Authors who are presenting talks have a * after their name.
