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Flexible copula model for integrating correlated multi‐omics data from single‐cell experiments

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  • Zichen Ma
  • Shannon W. Davis
  • Yen‐Yi Ho

Abstract

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.

Suggested Citation

  • Zichen Ma & Shannon W. Davis & Yen‐Yi Ho, 2023. "Flexible copula model for integrating correlated multi‐omics data from single‐cell experiments," Biometrics, The International Biometric Society, vol. 79(2), pages 1559-1572, June.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:2:p:1559-1572
    DOI: 10.1111/biom.13701
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    1. Jun Chen & Jichun Xie & Hongzhe Li, 2011. "A Penalized Likelihood Approach for Bivariate Conditional Normal Models for Dynamic Co-expression Analysis," Biometrics, The International Biometric Society, vol. 67(1), pages 299-308, March.
    2. Lianming Wang & David B. Dunson, 2010. "Semiparametric Bayes Multiple Testing: Applications to Tumor Data," Biometrics, The International Biometric Society, vol. 66(2), pages 493-501, June.
    3. Peter Xue‐Kun Song, 2000. "Multivariate Dispersion Models Generated From Gaussian Copula," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(2), pages 305-320, June.
    4. Yen-Yi Ho & Giovanni Parmigiani & Thomas A. Louis & Leslie M. Cope, 2011. "Modeling Liquid Association," Biometrics, The International Biometric Society, vol. 67(1), pages 133-141, March.
    5. Quan Zhou & Mingwei Liu & Xia Xia & Tongqing Gong & Jinwen Feng & Wanlin Liu & Yang Liu & Bei Zhen & Yi Wang & Chen Ding & Jun Qin, 2017. "A mouse tissue transcription factor atlas," Nature Communications, Nature, vol. 8(1), pages 1-15, April.
    6. Michael Pitt & David Chan & Robert Kohn, 2006. "Efficient Bayesian inference for Gaussian copula regression models," Biometrika, Biometrika Trust, vol. 93(3), pages 537-554, September.
    7. Michael S. Smith & Mohamad A. Khaled, 2012. "Estimation of Copula Models With Discrete Margins via Bayesian Data Augmentation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 290-303, March.
    8. Peter Muller & Giovanni Parmigiani & Christian Robert & Judith Rousseau, 2004. "Optimal Sample Size for Multiple Testing: The Case of Gene Expression Microarrays," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 990-1001, December.
    9. Hanqing Liu & Jingtian Zhou & Wei Tian & Chongyuan Luo & Anna Bartlett & Andrew Aldridge & Jacinta Lucero & Julia K. Osteen & Joseph R. Nery & Huaming Chen & Angeline Rivkin & Rosa G. Castanon & Ben C, 2021. "DNA methylation atlas of the mouse brain at single-cell resolution," Nature, Nature, vol. 598(7879), pages 120-128, October.
    10. Cribari-Neto, Francisco & Zeileis, Achim, 2010. "Beta Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i02).
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