On the Widespread and Critical Impact of Systematic Bias and Batch Effects in Single-Cell RNA-Seq Data

• Activity Number: 142 - Recent Statistical Advances in Single-Cell RNA-Seq Analysis
• Type: Invited
• Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
• Sponsor: Committee on Award of Outstanding Statistical Application
• Author(s): Rafael Irizarry*
• Companies: Harvard School of Public Health

Abstract

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.