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

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/. [Display omitted] •Proper normalization of scRNA-seq datasets is critical for fair interpretation•Different scRNA-seq datasets require different normalization strategies•“scone” assesses and ranks normalization methods according to their performance•High-ranking methods show better agreement with independent validation data 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.