Scone – 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. Researchers at UC Berkeley 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. The developers show that top-performing normalization methods lead to better agreement with independent validation data for a collection of scRNA-seq datasets.

Availability – scone can be downloaded at

Cole MB, Risso D, Wagner A, DeTomaso D, Ngai J, Purdom E, Dudoit S, Yosef N. (2019) Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq. Cell Syst 8(4):315-328.e8. [abstract]

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