Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells
Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells
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DOI：
10.1038/nbt.3102
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发表时间：
2015-02-01
影响因子：
46.9
通讯作者：
Stegie, Oliver
中科院分区：
文献类型：
--
作者：
Buettner, Florian;Natarajan, Kedar N.;Stegie, Oliver
最近的技术发展使数百个细胞的转录组能够以一种公正的方式进行分析，从而开辟了发现新的细胞亚群的可能性。然而，潜在的混杂因素，如细胞周期，对基因表达异质性的影响，从而对亚群识别能力的影响仍不清楚。我们提出并验证了一种使用潜在变量模型来解释这些隐藏因素的计算方法。我们的研究表明，我们的单细胞潜在变量模型（scLVM）可以识别在幼稚T细胞向辅助T 2细胞分化的不同阶段对应的其他不可检测的细胞亚群。我们的方法不仅可以用来鉴定细胞亚群，还可以用来梳理单细胞转录组中基因表达异质性的不同来源。
Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.