PT  - JOURNAL ARTICLE
AU  - Stephanie C. Hicks
AU  - Mingxiang Teng
AU  - Rafael A. Irizarry
TI  - On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data
AID  - 10.1101/025528
DP  - 2015 Jan 01
TA  - bioRxiv
PG  - 025528
4099  - http://biorxiv.org/content/early/2015/09/04/025528.short
4100  - http://biorxiv.org/content/early/2015/09/04/025528.full
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 five published studies 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 the implemented experimental designs confounded outcomes of interest with batch effects, a design that can bring into question some of the conclusions of these studies. Finally, we propose a simple experimental design that can ameliorate the effect of theses systematic errors have on downstream results.