Flexible copula model for integrating correlated multi-omics data from single-cell experiments.
Flexible copula model for integrating correlated multi-omics data from single-cell experiments.
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DOI：
10.1111/biom.13701
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发表时间：
2023-06
期刊：
影响因子：
1.9
通讯作者：
Ho, Yen-Yi
中科院分区：
文献类型：
--
作者：
Ma, Zichen;Davis, Shannon W.;Ho, Yen-Yi
关键词：
随着在单细胞水平上对多组学数据进行分析的技术的最新进展，综合多组学数据分析已经越来越流行。在单细胞实验中联合收集甲基化变化、染色质可及性和基因表达等信息越来越普遍。在生物医学研究中，研究各种数据类型之间的关联，并检查这些关联如何根据其他因素（如细胞类型和基因调控成分）发生变化，通常是令人感兴趣的。然而，由于每种数据类型通常具有不同的边缘分布，因此使用多组学数据联合分析这些关联变化在统计上具有挑战性。在本文中，我们提出了一个灵活的Copula为基础的框架模型协变量依赖的相关结构独立于他们的边缘。此外，所提出的方法可以联合联合收割机各种各样的单变量边际分布，离散或连续，包括类的零膨胀分布。所提出的框架的性能证明，通过一系列的模拟研究。最后，将其应用于一组实验数据，研究小鼠原肠胚形成期间不同种系的单细胞RNA测序、染色质可及性和DNA甲基化之间的动态关系。
With recent advances in technologies to profile multi-omics data at the single-cell level, integrative multi-omics data analysis has been increasingly popular. It is increasingly common that information such as methylation changes, chromatin accessibility, and gene expression are jointly collected in a single-cell experiment. In biomedical studies, it is often of interest to study the associations between various data types and to examine how these associations might change according to other factors such as cell types and gene regulatory components. However, since each data type usually has a distinct marginal distribution, joint analysis of these changes of associations using multi-omics data is statistically challenging. In this paper, we propose a flexible copula-based framework to model covariate-dependent correlation structures independent of their marginals. In addition, the proposed approach could jointly combine a wide variety of univariate marginal distributions, either discrete or continuous, including the class of zero-inflated distributions. The performance of the proposed framework is demonstrated through a series of simulation studies. Finally, it is applied to a set of experimental data to investigate the dynamic relationship between single-cell RNA-sequencing, chromatin accessibility, and DNA methylation at different germ layers during mouse gastrulation.
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