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Activity Number: 527 - Novel Statistical Methods for Single-Cell Genomic Data
Type: Invited
Date/Time: Thursday, August 11, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #319235
Title: Individual-Level Differential Expression Analysis for Single Cell RNA-Seq Data
Author(s): Mengqi Zhang and Si Liu and Zhen Miao and Fang Han and Raphael Gottardo and Wei Sun*
Companies: 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
Keywords: scRNA-seq; IDEAS; differential expression
Abstract:

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.


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

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