|
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
|
|
Abstract:
|
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.
Back to the full JSM 2019 program
|
