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Activity Number: 648 - Are Statistical Methods Developed for Bulk RNAseq Data Appropriate for Single Cell Data Sets?
Type: Topic Contributed
Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #305233
Title: Assumptions and Methods for Normalizing Single-Cell RNA-Seq Data
Author(s): Rhonda Bacher*
Companies: University of Florida
Keywords: single-cell; RNA-seq; normalization; scRNA-seq

Normalization of RNA-seq data is essential for accurate downstream inference, but the assumptions upon which traditional bulk normalization methods are based are not designed to accommodate unique features present in single-cell RNA-seq data (scRNA-seq). Here, we show that bulk RNA-seq normalization methods are not suitable for single-cell RNA-seq data in many cases and introduce additional artifacts. We develop SCnorm to enable accurate normalization for scRNA-seq data. Simulation and case study results demonstrate that SCnorm provides for increased accuracy in fold-change estimation and improvements in downstream inference. We also present a novel evaluation criterion of successful normalization applicable to both the bulk and single-cell setting.

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

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