Description

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

Po-Yuan Tung¹, John D. Blischak^1,2, Chiaowen Joyce Hsiao¹, David A. Knowles^3,4, Jonathan Burnett¹, Jonathan K. Pritchard^3,5,6, Yoav Gilad^1,7,*

¹Department of Human Genetics, University of Chicago, Chicago, Illinois, USA; ²Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, Illinois, USA;
³Department of Genetics, Stanford University, Stanford, CA, USA; ⁴Department of Radiology, Stanford University, Stanford, CA, USA; ⁵Department of Biology, Stanford University, Stanford, CA, USA; ⁶Howard Hughes Medical Institute, Stanford University, CA, USA; ⁷Department of Medicine, University of Chicago, Chicago, Illinois, USA

^*Corresponding author

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.