An important challenge in pre-processing data from droplet-based single-cell RNA sequencing protocols is distinguishing barcodes associated with real cells from those binding background reads. Existing methods test barcodes individually and consequently do not leverage the strong cell-to-cell correlation present in most datasets. To improve cell detection, we introduce CB2, a cluster-based approach for distinguishing real cells from background barcodes. As demonstrated in simulated and case study datasets, CB2 has increased power for identifying real cells which allows for the identification of novel subpopulations and improves the precision of downstream analyses.

中文翻译：

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CB2 提高了基于液滴的单细胞 RNA 测序数据中细胞检测的能力

预处理来自基于液滴的单细胞 RNA 测序协议的数据的一个重要挑战是将与真实细胞相关的条形码与那些结合背景读数区分开来。现有方法单独测试条形码，因此不会利用大多数数据集中存在的强细胞间相关性。为了改进细胞检测，我们引入了 CB2，一种基于集群的方法，用于区分真实细胞和背景条形码。正如模拟和案例研究数据集所证明的那样，CB2 增强了识别真实细胞的能力，从而可以识别新的亚群并提高下游分析的精度。