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

Cole, M. B.; Risso, D.; Wagner, A.; DeTomaso, D.; Ngai, J.; Purdom, E.; Dudoit, S.; Yosef, N.

doi:10.1101/235382

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bioRxiv · 10.1101/235382

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

Cole, M. B.·Risso, D.·Wagner, A.·DeTomaso, D.·Ngai, J.·Purdom, E.·Dudoit, S.·Yosef, N.

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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.\n\nWe 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.

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Cole, M. B., Risso, D., Wagner, A., DeTomaso, D., Ngai, J., Purdom, E., Dudoit, S., Yosef, N.. 2017-12-16. Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq. https://doi.org/10.1101/235382

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