SCOUP: a probabilistic model based on the Ornstein-Uhlenbeck process to analyze single-cell expression data during differentiation.
SCOUP: a probabilistic model based on the Ornstein-Uhlenbeck process to analyze single-cell expression data during differentiation.
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DOI：
10.1186/s12859-016-1109-3
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发表时间：
2016-06-08
影响因子：
3
通讯作者：
Kiryu H
中科院分区：
文献类型：
--
作者：
Matsumoto H;Kiryu H
单细胞技术使量化单个细胞的综合状态成为可能，特别是有能力揭示细胞分化。虽然已经开发了几种方法来全面分析单细胞表达数据，但在分化分析方面仍有改进的空间。在本文中，我们提出了一种新的方法SCOUP来阐明分化过程。与以前基于降维的方法不同，SCOUP直接描述了整个分化过程中基因表达的动态，包括细胞的分化程度（在伪时间内）和细胞命运。SCOUP在伪时间估计方面上级以前的方法，特别是对于单细胞RNA-seq。SCOUP也成功地估计细胞谱系更准确地比以前的方法，特别是对细胞在早期阶段的分叉。此外，SCOUP还可应用于各种下游分析。作为一个例子，我们提出了一种新的相关性计算方法来阐明基因之间的调控关系。我们将这种方法应用于单细胞RNA-seq数据，并检测相关网络中的分化和聚类的关键调节子的候选者，这是传统相关分析无法检测到的。我们开发了一种基于随机过程的方法SCOUP来分析整个分化过程中的单细胞表达数据。SCOUP方法可以更准确地估计伪时间和细胞谱系。我们还提出了一种新的相关性计算方法的基础上SCOUP。SCOUP是用于进一步单细胞分析的有前途的方法，可在https://github.com/hmatsu1226/SCOUP上获得。本文的在线版本（doi：10.1186/s12859-016-1109-3）包含补充材料，可供授权用户使用。
Single-cell technologies make it possible to quantify the comprehensive states of individual cells, and have the power to shed light on cellular differentiation in particular. Although several methods have been developed to fully analyze the single-cell expression data, there is still room for improvement in the analysis of differentiation. In this paper, we propose a novel method SCOUP to elucidate differentiation process. Unlike previous dimension reduction-based approaches, SCOUP describes the dynamics of gene expression throughout differentiation directly, including the degree of differentiation of a cell (in pseudo-time) and cell fate. SCOUP is superior to previous methods with respect to pseudo-time estimation, especially for single-cell RNA-seq. SCOUP also successfully estimates cell lineage more accurately than previous method, especially for cells at an early stage of bifurcation. In addition, SCOUP can be applied to various downstream analyses. As an example, we propose a novel correlation calculation method for elucidating regulatory relationships among genes. We apply this method to a single-cell RNA-seq data and detect a candidate of key regulator for differentiation and clusters in a correlation network which are not detected with conventional correlation analysis. We develop a stochastic process-based method SCOUP to analyze single-cell expression data throughout differentiation. SCOUP can estimate pseudo-time and cell lineage more accurately than previous methods. We also propose a novel correlation calculation method based on SCOUP. SCOUP is a promising approach for further single-cell analysis and available at https://github.com/hmatsu1226/SCOUP. The online version of this article (doi:10.1186/s12859-016-1109-3) contains supplementary material, which is available to authorized users.