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
中科院分区:
文献类型:
--
作者:
Boukouvalas A;Hensman J;Rattray M
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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