SiftCell: A robust framework to detect and isolate cell-containing droplets from single-cell RNA sequence reads.
SiftCell: A robust framework to detect and isolate cell-containing droplets from single-cell RNA sequence reads.
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SiftCell：一个强大的框架，用于从单细胞 RNA 序列读取中检测和分离含有细胞的液滴。
DOI：
10.1016/j.cels.2023.06.002
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发表时间：
2023
期刊：
影响因子：
9.3
通讯作者：
Kang,HyunMin
中科院分区：
文献类型：
--
作者：
Xi,Jingyue;Park,SungRye;Lee,JunHee;Kang,HyunMin
单细胞RNA测序(scRNA-seq)大量分析了平行包裹在条形码液滴中的单个细胞的转录本。然而，在现实世界的scRNA-seq数据中，许多条形码液滴并不包含细胞，相反，它们捕获了从受损或裂解的细胞释放的一小部分环境RNA。分析scRNA-seq数据的一个典型的第一步是过滤出无细胞的液滴并分离出含有细胞的液滴，但区分它们往往具有挑战性；不正确的过滤可能会在很大程度上误导下游分析。我们提出了SiftCell，一套通过随机化(SiftCell-Shuffle)识别和可视化流形空间中含细胞和无细胞液滴的软件工具(SiftCell-Shuffle)，以区分两种类型的液滴(SiftCell-Boost)，并量化环境RNA对每个液滴的贡献(SiftCell-Mix)。通过对不同单细胞平台获得的数据集的应用，我们发现SiftCell提供了一种简化的方法来执行scRNA-seq的上游质量控制，比现有的方法更全面和准确。
Single-cell RNA sequencing (scRNA-seq) massively profiles transcriptomes of individual cells encapsulated in barcoded droplets in parallel. However, in real-world scRNA-seq data, many barcoded droplets do not contain cells, but instead, they capture a fraction of ambient RNAs released from damaged or lysed cells. A typical first step to analyze scRNA-seq data is to filter out cell-free droplets and isolate cell-containing droplets, but distinguishing them is often challenging; incorrect filtering may mislead the downstream analysis substantially. We proposeSiftCell, a suite of software tools to identify and visualize cell-containing and cell-free droplets in manifold space via randomization (SiftCell-Shuffle) to classify between the two types of droplets (SiftCell-Boost) and to quantify the contribution of ambient RNAs for each droplet (SiftCell-Mix). By applying our method to datasets obtained by various single-cell platforms, we show thatSiftCellprovides a streamlined way to perform upstream quality control of scRNA-seq, which is more comprehensive and accurate than existing methods.