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/2015/12/27/025528.short 4100 - http://biorxiv.org/content/early/2015/12/27/025528.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 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.