Single-cell RNA sequencing has enabled the characterization of highly specific cell types in many tissues, as well as both primary and stem cell-derived cell lines. An important facet of these studies is the ability to identify the transcriptional signatures that define a cell type or state. In theory, this information can be used to classify an individual cell based on its transcriptional profile. Here, we present scPred, a new generalizable method that is able to provide highly accurate classification of single cells, using a combination of unbiased feature selection from a reduced-dimension space, and machine-learning probability-based prediction method. We apply scPred to scRNA-seq data from pancreatic tissue, mononuclear cells, colorectal tumor biopsies, and circulating dendritic cells and show that scPred is able to classify individual cells with high accuracy. The generalized method is available at https://github.com/powellgenomicslab/scPred/.

中文翻译：

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scPred：根据单细胞 RNA-seq 数据进行细胞类型分类的准确监督方法


单细胞 RNA 测序能够表征许多组织中高度特异性的细胞类型，以及原代细胞系和干细胞衍生的细胞系。这些研究的一个重要方面是能够识别定义细胞类型或状态的转录特征。理论上，该信息可用于根据转录谱对单个细胞进行分类。在这里，我们提出了 scPred，这是一种新的通用方法，它结合使用降维空间中的无偏特征选择和基于机器学习概率的预测方法，能够提供高度准确的单细胞分类。我们将 scPred 应用于来自胰腺组织、单核细胞、结直肠肿瘤活检和循环树突状细胞的 scRNA-seq 数据，并表明 scPred 能够以高精度对单个细胞进行分类。通用方法可在 https://github.com/powellgenomicslab/scPred/ 获得。