Batch effects and the effective design of single-cell gene expression studies

Po-Yuan Tung,John D Blischak,Jonathan K Pritchard,Chiaowen Joyce Hsiao,David A Knowles,Yoav Gilad,Jonathan E Burnett

doi:10.1038/srep39921

Abstract

Single-cell RNA sequencing (scRNA-seq) can be used to characterize variation in gene expression levels at high resolution. However, the sources of experimental noise in scRNA-seq are not yet well understood. We investigated the technical variation associated with sample processing using the single-cell Fluidigm C1 platform. To do so, we processed three C1 replicates from three human induced pluripotent stem cell (iPSC) lines. We added unique molecular identifiers (UMIs) to all samples, to account for amplification bias. We found that the major source of variation in the gene expression data was driven by genotype, but we also observed substantial variation between the technical replicates. We observed that the conversion of reads to molecules using the UMIs was impacted by both biological and technical variation, indicating that UMI counts are not an unbiased estimator of gene expression levels. Based on our results, we suggest a framework for effective scRNA-seq studies.

Highlights

Single-cell RNA sequencing can be used to characterize variation in gene expression levels at high resolution
We added External RNA Controls Consortium (ERCC) spike-in controls to each sample, and used 5-bp random sequence unique molecule identifiers (UMIs) to allow for the direct quantification of mRNA molecule numbers
Our nested study design allowed us to explicitly estimate technical batch effects associated with single cell sample processing on the C1 platform

Summary

Introduction

Single-cell RNA sequencing (scRNA-seq) can be used to characterize variation in gene expression levels at high resolution. A fundamental difficulty, for instance, is the presence of inevitable technical variability introduced during sample processing steps, including but not limited to the conditions of mRNA capture from a single cell, amplification bias, sequencing depth, and variation in pipetting accuracy. These (and other sources of error) may not be unique to single cell technologies, but in the context of studies where each sample corresponds to a single cell, and is processed as a single unrepeatable batch, these technical considerations make the analysis of biological variability across single cells challenging.

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