Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data.
Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data.
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DOI：
10.1038/s41467-018-04696-6
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发表时间：
2018-06-22
影响因子：
16.6
通讯作者：
Yau C
中科院分区：
文献类型：
--
作者：
Campbell KR;Yau C
伪时间算法可以被用来从横截面数据集中提取潜在的时间信息，从而允许在时间序列数据的收集具有挑战性或令人望而却步的情况下研究动态生物过程。计算技术出现在单细胞组学和癌症模型中，在这些模型中，伪时间可用于了解细胞分化或肿瘤进展。然而，迄今为止的方法通常隐含地假设同质的遗传、表型或环境背景，随着数据集的大小和复杂性的增长，这变得有限。我们描述了一个新的统计框架，该框架学习如何通过编码这些因素的协变量来调制伪时间轨迹。我们将该模型应用于单细胞和批量基因表达数据集，并表明该方法可以恢复已知和新的协变量-伪时间交互效应。这种混合回归-潜变量模型框架将伪时态模型从其最流行的单细胞基因组学领域扩展到更广泛的应用领域。横切面基因组数据通常具有非同质性的遗传、表型或环境背景。在这里，作者开发了一个统计框架，在单细胞和整体基因表达分析中，在存在这些因素及其相互作用的情况下推断假时间轨迹
Pseudotime algorithms can be employed to extract latent temporal information from cross-sectional data sets allowing dynamic biological processes to be studied in situations where the collection of time series data is challenging or prohibitive. Computational techniques have arisen from single-cell ‘omics and cancer modelling where pseudotime can be used to learn about cellular differentiation or tumour progression. However, methods to date typically implicitly assume homogeneous genetic, phenotypic or environmental backgrounds, which becomes limiting as data sets grow in size and complexity. We describe a novel statistical framework that learns how pseudotime trajectories can be modulated through covariates that encode such factors. We apply this model to both single-cell and bulk gene expression data sets and show that the approach can recover known and novel covariate-pseudotime interaction effects. This hybrid regression-latent variable model framework extends pseudotemporal modelling from its most prevalent area of single cell genomics to wider applications. Cross-sectional omic data often have non-homogeneous genetic, phenotypic, or environmental backgrounds. Here, the authors develop a statistical framework to infer pseudotime trajectories in the presence of such factors as well as their interactions in both single-cell and bulk gene expression analysis
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影响因子：
8.8
作者：
Gille, JJP;Hogervorst, FBL;Pals, G;Wijnen, JT;van Schooten, RJ;Dommering, CJ;Meijer, GA;Craanen, ME;Nederlof, PM;de Jong, D;McElgunn, CJ;Schouten, JP;Menko, FH
通讯作者：
Menko, FH
影响因子：
29.4
作者：
Boland CR;Goel A
通讯作者：
Goel A
影响因子：
14.9
作者：
Ji Z;Ji H
通讯作者：
Ji H
影响因子：
4.3
作者：
Campbell KR;Yau C
通讯作者：
Yau C
影响因子：
168.9
作者：
Davies, C.;Godwin, J.;Gray, R.;Clarke, M.;Darby, S.;McGale, P.;Wang, Y. C.;Peto, R.;Pan, H. C.;Cutter, D.;Taylor, C.;Ingle, J.
通讯作者：
Ingle, J.