SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species
SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species
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DOI：
10.1016/j.cels.2019.06.004
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发表时间：
2019-08-28
期刊：
影响因子：
9.3
通讯作者：
Cahan, Patrick
中科院分区：
文献类型：
--
作者：
Tan, Yuqi;Cahan, Patrick
单细胞RNA-seq已成为多种应用中的强大工具，从确定组织的细胞类型组成到揭示发育程序的调节因子。在单细胞RNA-seq数据分析中，一个几乎通用的步骤是假设每个细胞的身份。通常，这是通过搜索先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-seq研究。在这里，我们描述了我们的工具SingleCellNet，它解决了这些问题，并使查询单细胞RNA-seq数据与参考单细胞RNA-seq数据相比能够进行分类。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。我们强调SingleCellNet的效用分类以前未确定的细胞，并通过评估细胞命运工程实验的结果。
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.