Journal
CELL SYSTEMS
Volume 8, Issue 4, Pages 315-+Publisher
CELL PRESS
DOI: 10.1016/j.cels.2019.03.010
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Funding
- National Institutes of Health, BRAIN Initiative [U01 MH105979, U19 MH114830]
- Chan Zuck-erberg Initiative DAF, an advised fund of Silicon Valley Community Foundation [2018-183201]
- Programma per Giovani Ricercatori Rita Levi Montalcini - Italian Ministry of Education, University, and Research
- National Institute of Dental and Craniofacial Research (NIDCR) [F31 DE025176]
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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 opensource Bioconductor R software package scone. Weshow 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/.
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