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

Michael B. Cole, Davide Risso, Allon Wagner, David DeTomaso, John Ngai, Elizabeth Purdom, Sandrine Dudoit, Nir Yosef
doi: https://doi.org/10.1101/235382
Michael B. Cole
1Department of Physics, University of California, Berkeley
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Davide Risso
2Division of Biostatistics and Epidemiology, Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY
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Allon Wagner
3Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
4Center for Computational Biology, University of California, Berkeley
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David DeTomaso
4Center for Computational Biology, University of California, Berkeley
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John Ngai
5Department of Molecular and Cell Biology, University of California, Berkeley
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Elizabeth Purdom
4Center for Computational Biology, University of California, Berkeley
6Department of Statistics, University of California, Berkeley
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Sandrine Dudoit
4Center for Computational Biology, University of California, Berkeley
6Department of Statistics, University of California, Berkeley
7Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley
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Nir Yosef
3Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
4Center for Computational Biology, University of California, Berkeley
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Abstract

Systematic measurement biases make data normalization an essential preprocessing step in single-cell RNA sequencing (scRNA-seq) analysis. There may be multiple, competing considerations behind the assessment of normalization performance, some of them study-specific. Because normalization can have a large impact on downstream results (e.g., clustering and differential expression), it is critically important that practitioners assess the performance of competing methods.

We have developed scone — a flexible framework for assessing normalization performance based on a comprehensive panel of data-driven metrics. Through graphical summaries and quantitative reports, scone summarizes performance trade-offs and ranks large numbers of normalization methods by aggregate panel performance. The method is implemented in the open-source Bioconductor R software package scone. We demonstrate the effectiveness of scone on a collection of scRNA-seq datasets, generated with different protocols, including Fluidigm C1 and 10x platforms. We show that top-performing normalization methods lead to better agreement with independent validation data.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted May 18, 2018.
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Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-seq
Michael B. Cole, Davide Risso, Allon Wagner, David DeTomaso, John Ngai, Elizabeth Purdom, Sandrine Dudoit, Nir Yosef
bioRxiv 235382; doi: https://doi.org/10.1101/235382
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Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-seq
Michael B. Cole, Davide Risso, Allon Wagner, David DeTomaso, John Ngai, Elizabeth Purdom, Sandrine Dudoit, Nir Yosef
bioRxiv 235382; doi: https://doi.org/10.1101/235382

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