Differential Markov random field analysis with an application to detecting differential microbial community networks

Cai, Tommaso; Li, H; Ma, Jing; Yin, Xia

doi:10.1093/biomet/asz012

Cited by 24 publications

(26 citation statements)

References 31 publications

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“…Thus, fitting Gaussian graphical models onto it may not be suitable. Besides, it is difficult to interpret such models, and the graphical models discussed here do not assign statistical significance or uncertainties onto the estimated conditional correlations [91].…”

Section: Conditional Correlation Networkmentioning

confidence: 98%

Statistical computation methods for microbiome compositional data network inference

Chen¹,

He²,

Wan³

et al. 2021

Preprint

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Microbes can survive in some extreme environments and can be found almost everywhere in the world. Microbial communities have been found to be associated with higher live forms, including animals and plants. Microbes can affect processes from food production to human health, such as disease and homeostasis. Such microbes are not isolated, but rather interact with each other and establish connections with their living environments. Understanding these interactions is essential to an understanding of the organization and complex interplay of microbial communities, as well as the structure and dynamics of various ecosystems. A common and essential approach toward this objective involves the inference of microbiome interaction networks. Although network inference methods in other fields have been studied before, applying these methods to estimate microbiome associations based on compositional data will not yield valid results. On the one hand, features of microbiome data such as compositionality, sparsity and high-dimensionality challenge the data normalization and the design of computational methods. On the other hand, several issues like microbial community heterogeneity, external environmental interference and biological concerns also make it more difficult to deal with the network inference. In this paper, we provide a comprehensive review of emerging microbiome interaction network inference methods. According to various assumptions and research targets, estimated networks are divided into four main categories: correlation networks, conditional correlation networks, mixture networks and differential networks. Their scope of applications, advantages, as well as limitations, are presented in this review. Since real microbial interactions can be complex and dynamic, no unifying method has, to date, captured all the aspects of interest. In addition, we discuss the challenges now confronting current microbial associations study and future prospects. Finally, we highlight that the research in microbial network inference requires the joint promotion of statistical computation methods and experimental techniques. Codes of most methods introduced in this review will be collected in https://github.com/Qiuyanhe/Statisticalcomputation-methods-for-microbiome-compositional-data-network-inference.

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Section: Conditional Correlation Networkmentioning

confidence: 98%

Statistical computation methods for microbiome compositional data network inference

Chen¹,

He²,

Wan³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Recent developments on statistical approaches for differential network analysis have started to focus on directed networks, and, in particular, directed acyclic graphs (DAGs) (Ghoshal & Honorio, 2019;Y. Wang et al, 2018), as well as graphical models for other data types (T. Cai, Li, Ma, & Xia, 2018;He et al, 2019;Kim, Liu, & Kolar, 2019;M. Yu, Gupta, et al, 2019;S.…”

Section: Further Readingmentioning

confidence: 99%

Differential network analysis: A statistical perspective

Shojaie

2020

WIREs Computational Stats

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Networks effectively capture interactions among components of complex systems, and have thus become a mainstay in many scientific disciplines. Growing evidence, especially from biology, suggest that networks undergo changes over time, and in response to external stimuli. In biology and medicine, these changes have been found to be predictive of complex diseases. They have also been used to gain insight into mechanisms of disease initiation and progression. Primarily motivated by biological applications, this article provides a review of recent statistical machine learning methods for inferring networks and identifying changes in their structures. * To appear as an Advanced Review in WIREs Computational Statistics. arXiv:2003.04235v1 [stat.ME]

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“…First, the majority of the literature on graphical models are developed assuming a particular observation model, and the growing literature on difference estimation is no exception. For example, Xia et al (2015) assume that the data are Gaussian, whereas Cai et al (2019) use an Ising model. By contrast, we work with general Markov random fields; we present a unified framework for statistical inference in differential networks, without the need for developing separate methodology for different distributional assumptions.…”

Section: Introductionmentioning

confidence: 99%

Two-Sample Inference for High-Dimensional Markov Networks

Kim

Liu

Kolar

2021

Journal of the Royal Statistical Society Series B: Statistical Methodology

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Markov networks are frequently used in sciences to represent conditional independence relationships underlying observed variables arising from a complex system. It is often of interest to understand how an underlying network differs between two conditions. In this paper, we develop methods for comparing a pair of high‐dimensional Markov networks where we allow the number of observed variables to increase with the sample sizes. By taking the density ratio approach, we are able to learn the network difference directly and avoid estimating the individual graphs. Our methods are thus applicable even when the individual networks are dense as long as their difference is sparse. We prove finite‐sample Gaussian approximation error bounds for the estimator we construct under significantly weaker assumptions than are typically required for model selection consistency. Furthermore, we propose bootstrap procedures for estimating quantiles of a max‐type statistics based on our estimator, and show how they can be used to test the equality of two Markov networks or construct simultaneous confidence intervals. The performance of our methods is demonstrated through extensive simulations. The scientific usefulness is illustrated with an analysis of a new fMRI data set.

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Differential Markov random field analysis with an application to detecting differential microbial community networks

Cited by 24 publications

References 31 publications

Statistical computation methods for microbiome compositional data network inference

Statistical computation methods for microbiome compositional data network inference

Differential network analysis: A statistical perspective

Two-Sample Inference for High-Dimensional Markov Networks

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