RT Journal Article
SR Electronic
T1 On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 025528
DO 10.1101/025528
A1 Stephanie C. Hicks
A1 Mingxiang Teng
A1 Rafael A. Irizarry
YR 2015
UL http://biorxiv.org/content/early/2015/12/27/025528.abstract
AB Single-cell RNA-Sequencing (scRNA-Seq) has become the most widely used high-throughput method for transcription profiling of individual cells. Systematic errors, including batch effects, have been widely reported as a major challenge in high-throughput technologies. Surprisingly, these issues have received minimal attention in published studies based on scRNA-Seq technology. We examined data from all fifteen published studies including at least 200 samples and found that systematic errors can explain a substantial percentage of observed cell-to-cell expression variability. Specifically, we found that the proportion of genes reported as expressed explains a substantial part of observed variability and that this quantity varies systematically across experimental batches. Furthermore, we found that experimental designs that confound outcomes of interest with batch effects are common. Finally, we propose a simple experimental design that can ameliorate the effect of theses systematic errors have on downstream results.