SingleCellNet：一种跨平台和跨物种对单细胞 RNA-Seq 数据进行分类的计算工具。
SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species.
机构信息
Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
出版信息
Cell Syst. 2019 Aug 28;9(2):207-213.e2. doi: 10.1016/j.cels.2019.06.004. Epub 2019 Jul 31.
Single-cell RNA-seq has emerged as a powerful tool in diverse applications, from determining the cell-type composition of tissues to uncovering regulators of developmental programs. A near-universal step in the analysis of single-cell RNA-seq data is to hypothesize the identity of each cell. Often, this is achieved by searching for combinations of genes that have previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single-cell RNA-seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single-cell RNA-seq data in comparison to reference single-cell RNA-seq data. SingleCellNet compares favorably to other methods in sensitivity and specificity, and it is able to classify across platforms and species. We highlight SingleCellNet's utility by classifying previously undetermined cells, and by assessing the outcome of a cell fate engineering experiment.
单细胞 RNA 测序在各种应用中已经成为一种强大的工具，从确定组织的细胞类型组成到揭示发育程序的调控因子。在单细胞 RNA 测序数据的分析中，几乎普遍的一步是假设每个细胞的身份。通常，这是通过搜索以前被认为是细胞类型特异性的基因组合来实现的，这种方法不是定量的，也没有明确利用其他单细胞 RNA 测序研究。在这里，我们描述了我们的工具 SingleCellNet，它解决了这些问题，并能够将查询单细胞 RNA 测序数据与参考单细胞 RNA 测序数据进行分类。SingleCellNet 在灵敏度和特异性方面优于其他方法，并且能够跨平台和物种进行分类。我们通过对以前未确定的细胞进行分类，并评估细胞命运工程实验的结果，突出了 SingleCellNet 的实用性。
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