PT  - JOURNAL ARTICLE
AU  - Cole, Michael B.
AU  - Risso, Davide
AU  - Wagner, Allon
AU  - DeTomaso, David
AU  - Ngai, John
AU  - Purdom, Elizabeth
AU  - Dudoit, Sandrine
AU  - Yosef, Nir
TI  - Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-seq
AID  - 10.1101/235382
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 235382
4099  - http://biorxiv.org/content/early/2018/05/18/235382.short
4100  - http://biorxiv.org/content/early/2018/05/18/235382.full
AB  - 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.We 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.