College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.
BGI-Shenzhen, Shenzhen, 518083, China.
Commun Biol. 2022 May 30;5(1):510. doi: 10.1038/s42003-022-03476-9.
High-throughput single-cell RNA sequencing (scRNA-seq) is a popular method, but it is accompanied by doublet rate problems that disturb the downstream analysis. Several computational approaches have been developed to detect doublets. However, most of these methods may yield satisfactory performance in some datasets but lack stability in others; thus, it is difficult to regard a single method as the gold standard which can be applied to all types of scenarios. It is a difficult and time-consuming task for researchers to choose the most appropriate software. We here propose Chord which implements a machine learning algorithm that integrates multiple doublet detection methods to address these issues. Chord had higher accuracy and stability than the individual approaches on different datasets containing real and synthetic data. Moreover, Chord was designed with a modular architecture port, which has high flexibility and adaptability to the incorporation of any new tools. Chord is a general solution to the doublet detection problem.
高通量单细胞 RNA 测序 (scRNA-seq) 是一种流行的方法，但它伴随着干扰下游分析的双细胞率问题。已经开发了几种计算方法来检测双细胞。然而，这些方法中的大多数在某些数据集上可能表现出令人满意的性能，但在其他数据集上缺乏稳定性；因此，很难将单一方法视为适用于所有类型场景的黄金标准。研究人员选择最合适的软件是一项困难且耗时的任务。我们在这里提出 Chord，它实现了一种机器学习算法，该算法集成了多种双细胞检测方法来解决这些问题。Chord 在包含真实和合成数据的不同数据集上的准确性和稳定性均高于单个方法。此外，Chord 具有模块化架构端口设计，具有高度的灵活性和适应性，可以集成任何新工具。Chord 是双细胞检测问题的通用解决方案。
