RT Journal Article
SR Electronic
T1 Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-seq
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 235382
DO 10.1101/235382
A1 Cole, Michael B.
A1 Risso, Davide
A1 Wagner, Allon
A1 DeTomaso, David
A1 Ngai, John
A1 Purdom, Elizabeth
A1 Dudoit, Sandrine
A1 Yosef, Nir
YR 2018
UL http://biorxiv.org/content/early/2018/05/18/235382.abstract
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.