Single-cell RNA-seq data of mammary gland epithelial cells from different gestational stages to detect and remove barcode swapping

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).条码互换（barcode swapping）会导致Illumina新型图案化流动槽测序仪上，多重化样本间的测序读段被错误标注。这可能会损害众多基因组检测的有效性，对于常规需将大量样本进行多重化处理的单细胞研究而言，该问题尤为突出。目前学界对单细胞转录组研究中条码互换的严重程度与潜在影响仍知之甚少。本研究采用两种统计学方法，对两个基于微孔板的单细胞RNA测序数据集里的互换读段占比进行了稳健量化。我们在HiSeq 4000测序仪上发现，样本间约有2.5%的读段被错误标注，该比例低于此前的研究报道。我们未发现各微孔板中游离条码浓度与读段互换占比之间存在相关性。此外，本研究证实，在基于微滴的单细胞RNA测序研究中，条码互换可能会产生复杂却属于人工假象的细胞文库。为消除此类人工假象，本研究利用数据中蕴含的组合复杂性，开发了一款算法，用于剔除10X Genomics实验中样本间发生互换的单个分子。该算法可在规避条码互换带来的混杂效应的同时，保障基于微滴的实验仍可使用前沿测序仪。 本数据集仓库包含与Griffiths等人（2018年）研究中基于微滴的单细胞RNA测序（scRNA-seq）数据集相关的测序文件。本数据集仅可用于技术分析，不过下文将简要介绍其生物学研究背景：乳腺是一类独特的器官，其发育的大部分阶段发生于青春期与成年期。解析不同乳腺上皮细胞的层级关系，以及它们如何受妊娠、泌乳与退化过程的调控，对于理解乳腺癌的发生发展至关重要。近期已有多项研究借助多种标志物，对成年乳腺内不同上皮细胞亚群进行富集、分离与特征解析。但上述所有研究均仅使用少量标志物来定义与追踪细胞群体。因此，亟需对不同发育阶段乳腺内的上皮细胞进行无偏倚且全面的特征描述。为此，我们采用单细胞RNA测序（scRNA-seq）技术，测定了四个成年发育阶段——未生育、妊娠中期、泌乳期以及断奶后（完全自然退化）——的单个乳腺上皮细胞的基因表达谱。