E-MTAB-6854
Detection and removal of barcode swapping in single-cell RNA-seq data
Barcode swapping results in the mislabeling of sequencing reads between multiplexed samples on the new patterned flow cell Illumina sequencing machines. This may compromise the validity of numerous genomic assays, especially for single-cell studies where many samples are routinely multiplexed together. The severity and consequences of barcode swapping for single-cell transcriptomic studies remain poorly understood. We have used two statistical approaches to robustly quantify the fraction of swapped reads in each of two plate-based single-cell RNA sequencing datasets. We found that approximately 2.5% of reads were mislabeled between samples on the HiSeq 4000 machine, which is lower than previous reports. We observed no correlation between the swapped fraction of reads and the concentration of free barcode across plates. Further- more, 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, exploiting the combinatorial complexity present in the data. This permits the continued use of cutting-edge sequencing machines for droplet-based experiments while avoiding the confounding effects of barcode swapping.  This data repository contains the sequencing files associated with the droplet based scRNA-seq dataset in Griffiths et al. (2018). The data presented here should purely used for technical analysis, the biological motivation is nonetheless briefly described in the following: The mammary gland is a unique organ as it undergoes most of its development during puberty and adulthood. Characterising the hierarchy of the various mammary epithelial cells and how they are regulated in response to gestation, lactation and involution is important for understanding how breast cancer develops. Recent studies have used numerous markers to enrich, isolate and characterise the different epithelial cell compartments within the adult mammary gland. However, in all of these studies only a handful of markers were used to define and trace cell populations. Therefore, there is a need for an unbiased and comprehensive description of mammary epithelial cells within the gland at different developmental stages. To this end we used single cell RNA sequencing (scRNAseq) to determine the gene expression profile of individual mammary epithelial cells across four adult developmental stages; nulliparous, mid gestation, lactation and post weaning (full natural involution).
Barcode swapping results in the mislabeling of sequencing reads between multiplexed samples on the new patterned flow cell Illumina sequencing machines. This may compromise the validity of numerous genomic assays, especially for single-cell studies where many samples are routinely multiplexed together. The severity and consequences of barcode swapping for single-cell transcriptomic studies remain poorly understood. We have used two statistical approaches to robustly quantify the fraction of swapped reads in each of two plate-based single-cell RNA sequencing datasets. We found that approximately 2.5% of reads were mislabeled between samples on the HiSeq 4000 machine, which is lower than previous reports. We observed no correlation between the swapped fraction of reads and the concentration of free barcode across plates. Further- more, 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, exploiting the combinatorial complexity present in the data. This permits the continued use of cutting-edge sequencing machines for droplet-based experiments while avoiding the confounding effects of barcode swapping.  This data repository contains the sequencing files associated with the droplet based scRNA-seq dataset in Griffiths et al. (2018). The data presented here should purely used for technical analysis, the biological motivation is nonetheless briefly described in the following: The mammary gland is a unique organ as it undergoes most of its development during puberty and adulthood. Characterising the hierarchy of the various mammary epithelial cells and how they are regulated in response to gestation, lactation and involution is important for understanding how breast cancer develops. Recent studies have used numerous markers to enrich, isolate and characterise the different epithelial cell compartments within the adult mammary gland. However, in all of these studies only a handful of markers were used to define and trace cell populations. Therefore, there is a need for an unbiased and comprehensive description of mammary epithelial cells within the gland at different developmental stages. To this end we used single cell RNA sequencing (scRNAseq) to determine the gene expression profile of individual mammary epithelial cells across four adult developmental stages; nulliparous, mid gestation, lactation and post weaning (full natural involution).