BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process.

BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process.
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DOI:
10.1186/s13059-018-1440-2
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发表时间:
2018-05-29
期刊:
影响因子:
12.3
通讯作者:
Rattray M
Rattray M
中科院分区:
生物学1区
文献类型:
--
作者:
Boukouvalas A;Hensman J;Rattray M

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高通量单细胞基因表达实验可用于通过伪时间方法揭示正在分化的细胞群的分支动态。我们开发了分支高斯过程(BGP),这是一种非参数模型,能够识别单个基因的分支动态,并提供每个基因与相关可信区域的分支时间的估计。我们证明了我们的方法在模拟数据、单细胞 RNA-seq 造血研究和使用液滴条形码生成的小鼠胚胎干细胞上的有效性。该方法对于单细胞数据中常见的高水平技术变化和丢失具有鲁棒性。本文的在线版本 (10.1186/s13059-018-1440-2) 包含补充材料,可供授权用户使用。
High-throughput single-cell gene expression experiments can be used to uncover branching dynamics in cell populations undergoing differentiation through pseudotime methods. We develop the branching Gaussian process (BGP), a non-parametric model that is able to identify branching dynamics for individual genes and provide an estimate of branching times for each gene with an associated credible region. We demonstrate the effectiveness of our method on simulated data, a single-cell RNA-seq haematopoiesis study and mouse embryonic stem cells generated using droplet barcoding. The method is robust to high levels of technical variation and dropout, which are common in single-cell data. The online version of this article (10.1186/s13059-018-1440-2) contains supplementary material, which is available to authorized users.
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