In recent years, the rapid development of single-cell RNA-sequencing (scRNA-seq) techniques enables the quantitative characterization of cell types at a single-cell resolution. With the explosive growth of the number of cells profiled in individual scRNA-seq experiments, there is a demand for novel computational methods for classifying newly-generated scRNA-seq data onto annotated labels. Although several methods have recently been proposed for the cell-type classification of single-cell transcriptomic data, such limitations as inadequate accuracy, inferior robustness, and low stability greatly limit their wide applications. We propose a novel ensemble approach, named EnClaSC, for accurate and robust cell-type classification of single-cell transcriptomic data. Through comprehensive validation experiments, we demonstrate that EnClaSC can not only be applied to the self-projection within a specific dataset and the cell-type classification across different datasets, but also scale up well to various data dimensionality and different data sparsity. We further illustrate the ability of EnClaSC to effectively make cross-species classification, which may shed light on the studies in correlation of different species. EnClaSC is freely available at https://github.com/xy-chen16/EnClaSC . EnClaSC enables highly accurate and robust cell-type classification of single-cell transcriptomic data via an ensemble learning method. We expect to see wide applications of our method to not only transcriptome studies, but also the classification of more general data.

中文翻译：

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EnClaSC：一种用于单细胞转录组的准确，稳健的细胞类型分类的新型集成方法。

近年来，单细胞RNA测序（scRNA-seq）技术的飞速发展使得能够以单细胞分辨率对细胞类型进行定量表征。随着在单个scRNA-seq实验中分析的细胞数量的爆炸性增长，需要一种新颖的计算方法，用于将新生成的scRNA-seq数据分类到带注释的标签上。尽管最近已经提出了几种方法用于单细胞转录组数据的细胞类型分类，但是诸如精度不足，鲁棒性差和稳定性低等限制极大地限制了它们的广泛应用。我们提出了一种新的集成方法，称为EnClaSC，用于单细胞转录组数据的准确和鲁棒的细胞类型分类。通过全面的验证实验，我们证明EnClaSC不仅可以应用于特定数据集中的自投影以及跨不同数据集的单元格类型分类，而且还可以很好地扩展到各种数据维度和不同数据稀疏性。我们进一步说明了EnClaSC有效进行跨物种分类的能力，这可能为不同物种相关性的研究提供启示。EnClaSC可从https://github.com/xy-chen16/EnClaSC免费获得。EnClaSC可通过集成学习方法对单细胞转录组数据进行高度准确和强大的细胞类型分类。我们希望看到我们的方法不仅可以用于转录组研究，而且可以用于更多常规数据的分类。