Batch effects and the effective design of single-cell gene expression studies
Po-Yuan Tung1, John D. Blischak1,2, Chiaowen Joyce Hsiao1, David A. Knowles3,4, Jonathan Burnett1, Jonathan K. Pritchard3,5,6, Yoav Gilad1,7,*
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
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.