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Cell Syst. 2019 Apr 24;8(4):315-328.e8. doi: 10.1016/j.cels.2019.03.010.

Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq.

Author information

1
Department of Physics, University of California, Berkeley, CA, USA. Electronic address: mbcole@berkeley.edu.
2
Department of Statistical Sciences, University of Padova, Padova, Italy; Division of Biostatistics and Epidemiology, Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY, USA. Electronic address: dar2062@med.cornell.edu.
3
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA; Center for Computational Biology, University of California, Berkeley, CA, USA.
4
Center for Computational Biology, University of California, Berkeley, CA, USA.
5
Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.
6
Center for Computational Biology, University of California, Berkeley, CA, USA; Department of Statistics, University of California, Berkeley, CA, USA.
7
Center for Computational Biology, University of California, Berkeley, CA, USA; Department of Statistics, University of California, Berkeley, CA, USA; Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA, USA.
8
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA; Center for Computational Biology, University of California, Berkeley, CA, USA. Electronic address: niryosef@berkeley.edu.

Abstract

Systematic measurement biases make normalization an essential step in single-cell RNA sequencing (scRNA-seq) analysis. There may be multiple competing considerations behind the assessment of normalization performance, of which some may be study specific. We have developed "scone"- a flexible framework for assessing performance based on a comprehensive panel of data-driven metrics. Through graphical summaries and quantitative reports, scone summarizes trade-offs and ranks large numbers of normalization methods by panel performance. The method is implemented in the open-source Bioconductor R software package scone. We show that top-performing normalization methods lead to better agreement with independent validation data for a collection of scRNA-seq datasets. scone can be downloaded at http://bioconductor.org/packages/scone/.

KEYWORDS:

RNA-seq; methods; normalization; preprocessing; quality control; scRNA-seq; single-cell

PMID:
31022373
PMCID:
PMC6544759
[Available on 2020-04-24]
DOI:
10.1016/j.cels.2019.03.010

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