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Activity Number: 654 - New Methodology Developments in Single Cell RNA-Seq
Type: Topic Contributed
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: International Chinese Statistical Association
Abstract #330881
Title: Noise Modeling and Denoising of UMI-Based Single Cell RNA Sequencing Data
Author(s): Nancy Zhang* and Mo Huang and Mingyao Li and Jingshu Wang
Companies: and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
Keywords: Imputation; Deconvolution; Single cell; RNA sequencing; Genomics
Abstract:

Cells are the basic biological units of multicellular organisms. Recent breakthroughs in single-cell RNA sequencing (scRNA-seq) have made possible the profiling of gene expression at the single-cell level, paving the way for exploring gene expression heterogeneity among cells. In this talk, I will explore the noise properties of unique molecular identifier (UMI)-based scRNA-seq data, and show, by comparisons to Fluorescent in situ RNA hybridization (RNA FISH), that a simple Poisson-based model is sufficient for recovering true underlying gene expression distributions using a deconvolution method called DESCEND. I will also describe SAVER, a gene expression recovery framework for scRNA-seq data. SAVER reduces the noise in the observed scRNA-seq data and "recovers" the expression concentrations for genes with zero read counts due to low efficiency sampling. I will illustrate how SAVER affects downstream analyses such as cell type clustering, differential expression analysis, and the assessment of gene-gene relationships.


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

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