RT Journal Article
SR Electronic
T1 Batch effects and the effective design of single-cell gene expression studies
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 062919
DO 10.1101/062919
A1 Po-Yuan Tung
A1 John D. Blischak
A1 Chiaowen Joyce Hsiao
A1 David A. Knowles
A1 Jonathan E. Burnett
A1 Jonathan K. Pritchard
A1 Yoav Gilad
YR 2016
UL http://biorxiv.org/content/early/2016/07/08/062919.abstract
AB 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.