Detection and removal of barcode swapping in single-cell RNA-seq data

Jonathan A. Griffiths, Arianne C. Richard, Karsten Bach, Aaron T. L. Lun () and John C. Marioni ()
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Jonathan A. Griffiths: University of Cambridge
Arianne C. Richard: University of Cambridge
Karsten Bach: University of Cambridge
Aaron T. L. Lun: University of Cambridge
John C. Marioni: University of Cambridge

Nature Communications, 2018, vol. 9, issue 1, 1-6

Abstract: Abstract Barcode swapping results in the mislabelling of sequencing reads between multiplexed samples on patterned flow-cell Illumina sequencing machines. This may compromise the validity of numerous genomic assays; however, the severity and consequences of barcode swapping remain poorly understood. We have used two statistical approaches to robustly quantify the fraction of swapped reads in two plate-based single-cell RNA-sequencing datasets. We found that approximately 2.5% of reads were mislabelled between samples on the HiSeq 4000, which is lower than previous reports. We observed no correlation between the swapped fraction of reads and the concentration of free barcode across plates. Furthermore, we have demonstrated that barcode swapping may generate complex but artefactual cell libraries in droplet-based single-cell RNA-sequencing studies. To eliminate these artefacts, we have developed an algorithm to exclude individual molecules that have swapped between samples in 10x Genomics experiments, allowing the continued use of cutting-edge sequencing machines for these assays.

Date: 2018
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DOI: 10.1038/s41467-018-05083-x

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