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Activity Number: 453 - Advances on the Analysis of Single-Cell Sequencing Data
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #304661
Title: From Bulk to Single-Cell RNA-Seq Data: Differential Gene Expression Analysis
Author(s): Jingyi Jessica Li* and Yiling Chen
Companies: University of California, Los Angeles and University of California, Los Angeles
Keywords: single-cell RNA sequencing; clustering; simpson's paradox; differential expression

The recent introduction of single-cell RNA-sequencing (scRNA-seq) has revolutionized biomedical sciences by revealing genome-wide gene expression levels within an individual cell. It for the first time enables the identification of differentially expressed (DE) genes between conditions at the single-cell level, bringing RNA-seq analyses to a new stage. In contrast to bulk RNA-seq data with homogeneous samples, the well-known heterogeneity in scRNA-seq data brings new challenges to DE analysis.

Here I will introduce scClust, a computational framework that discovers previously unknown cell type marker genes and matches cells of the same cell type under more than one conditions. Researchers can utilize scClust to first identify matched cells, and then perform DE analysis within each cell type. Compared to common practices that simply treat cells under the same condition as replicates, our recommended practice based on scClust exploits the extensive heterogeneity in scRNA-seq data and uncovers cell type specific DE genes that may have been missed otherwise.

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

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