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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/
. We have developed an approach for exploratory analysis and normalization
of scRNA-seq data that enables execution of a wide array of normalization
procedures and provides principled assessment of their performance based on a
comprehensive set of data-driven performance metrics.