Activity Number:
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527
- Novel Statistical Methods for Single-Cell Genomic Data
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Type:
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Invited
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Date/Time:
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Thursday, August 11, 2022 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #319235
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Title:
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Individual-Level Differential Expression Analysis for Single Cell RNA-Seq Data
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Author(s):
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Mengqi Zhang and Si Liu and Zhen Miao and Fang Han and Raphael Gottardo and Wei Sun*
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Companies:
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University of Pennsylvania and Fred Hutchinson Cancer Research Center and University of Washington and University of Washington and Lausanne University Hospital and Fred Hutchinson Cancer Research Center
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Keywords:
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scRNA-seq;
IDEAS;
differential expression
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Abstract:
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We consider an increasingly popular study design where single cell RNA-seq data are collected from multiple subjects and the question of interest is to find genes that have different expression between two groups of subjects, Towards this end, we propose a statistical method named IDEAS (Individual level Differential Expression Analysis for scRNA-seq). For each gene, IDEAS summarizes its expression in each subject by a distribution and then uses these distributions to assess differential expression between two groups of subjects . We apply IDEAS to assess gene expression difference of autism subjects versus controls, and COVID-19 patients with mild versus severe symptoms.
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Authors who are presenting talks have a * after their name.