BZINB Model-Based Pathway Analysis and Module Identification Facilitates Integration of Microbiome and Metabolome Data.
BZINB Model-Based Pathway Analysis and Module Identification Facilitates Integration of Microbiome and Metabolome Data.
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DOI：
10.3390/microorganisms11030766
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发表时间：
2023-03-16
期刊：
影响因子：
4.5
通讯作者：
Wu D
中科院分区：
文献类型：
--
作者：
Lin BM;Cho H;Liu C;Roach J;Ribeiro AA;Divaris K;Wu D
整合多组学数据是一个具有挑战性但必要的步骤，以促进我们对人类健康和疾病过程的生物学基础的理解。迄今为止，寻求整合多组学（例如，微生物组和代谢组）的研究采用简单的基于相关性的网络分析；然而，这些方法并不总是很适合微生物组分析，因为它们不能适应这些数据中通常存在的多余零。在本文中，我们介绍了一种基于二元零膨胀负二项（BZINB）模型的网络和模块分析方法，该方法解决了这一限制，并通过容纳多余的零来改进基于微生物组-代谢组相关性的模型拟合。我们使用基于儿童口腔健康的多组学研究（ZOE 2.0；调查早期儿童龋齿，ECC）的真实和模拟数据，发现基于BZINB模型的相关方法在近似微生物分类群与代谢物之间的潜在关系方面的准确性优于Spearman 's rank和Pearson相关性。新方法BZINB- immpath利用BZINB构建代谢物-物种和种-物种相关网络，并结合BZINB和基于相似性的聚类识别（即相关）物种模块。在相关网络和模块的扰动可以有效地测试组之间（即，健康和患病的研究参与者）。在将新方法应用于ZOE 2.0研究微生物组-代谢组数据后，我们发现，在健康和龋齿患者之间，与碳水化合物代谢物相关的几种与ecc相关的微生物分类群的生物学相关性存在差异。总之，我们发现BZINB模型是一种有用的替代Spearman或Pearson相关性来估计零膨胀双变量计数数据的潜在相关性，因此适用于多组学数据的综合分析，例如在微生物组和代谢组研究中遇到的数据。
Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome–metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental caries, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman’s rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath, facilitates the construction of metabolite–species and species–species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome–metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.
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