Advances in droplet-based single-cell RNA-sequencing (scRNA-seq) have dramatically increased throughput, allowing tens of thousands of cells to be routinely sequenced in a single experiment. In addition to cells, droplets capture cell-free “ambient” RNA predominantly caused by lysis of cells during sample preparation. Samples with high ambient RNA concentration can create challenges in accurately distinguishing cell-containing droplets and droplets containing ambient RNA. Current methods to separate these groups often retain a significant number of droplets that do not contain cells or empty droplets. Additionally, there are currently no methods available to detect droplets containing damaged cells, which comprise partially lysed cells, the original source of the ambient RNA. Here, we describe DropletQC, a new method that is able to detect empty droplets, damaged, and intact cells, and accurately distinguish them from one another. This approach is based on a novel quality control metric, the nuclear fraction, which quantifies for each droplet the fraction of RNA originating from unspliced, nuclear pre-mRNA. We demonstrate how DropletQC provides a powerful extension to existing computational methods for identifying empty droplets such as EmptyDrops. We implement DropletQC as an R package, which can be easily integrated into existing single-cell analysis workflows.

中文翻译：

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DropletQC：改进了单细胞 RNA-seq 数据中空液滴和受损细胞的识别

基于液滴的单细胞 RNA 测序 (scRNA-seq) 的进展显着提高了通量，允许在单个实验中对数以万计的细胞进行常规测序。除细胞外，液滴还捕获主要由样品制备过程中细胞裂解引起的无细胞“环境”RNA。具有高环境 RNA 浓度的样品可能会在准确区分含有细胞的液滴和含有环境 RNA 的液滴方面产生挑战。当前分离这些组的方法通常保留大量不包含细胞或空液滴的液滴。此外，目前没有可用的方法来检测含有受损细胞的液滴，这些细胞包括部分裂解的细胞，这是环境 RNA 的原始来源。在这里，我们描述 DropletQC，一种能够检测空液滴、受损和完整细胞并准确区分它们的新方法。这种方法基于一种新的质量控制指标，即核分数，该指标量化了每个液滴中源自未剪接的核前 mRNA 的 RNA 分数。我们展示了 DropletQC 如何为现有的用于识别空液滴（如 EmptyDrops）的计算方法提供强大的扩展。我们将 DropletQC 实现为 R 包，可以轻松集成到现有的单细胞分析工作流程中。我们展示了 DropletQC 如何为现有的用于识别空液滴（如 EmptyDrops）的计算方法提供强大的扩展。我们将 DropletQC 实现为 R 包，可以轻松集成到现有的单细胞分析工作流程中。我们展示了 DropletQC 如何为现有的用于识别空液滴（如 EmptyDrops）的计算方法提供强大的扩展。我们将 DropletQC 实现为 R 包，可以轻松集成到现有的单细胞分析工作流程中。