A hierarchical Bayesian approach for detecting global microbiome associations.

IF 0.9 4区 数学 Q3 Mathematics
Farhad Hatami, Emma Beamish, Albert Davies, Rachael Rigby, Frank Dondelinger
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引用次数: 0

Abstract

The human gut microbiome has been shown to be associated with a variety of human diseases, including cancer, metabolic conditions and inflammatory bowel disease. Current approaches for detecting microbiome associations are limited by relying on specific measures of ecological distance, or only allowing for the detection of associations with individual bacterial species, rather than the whole microbiome. In this work, we develop a novel hierarchical Bayesian model for detecting global microbiome associations. Our method is not dependent on a choice of distance measure, and is able to incorporate phylogenetic information about microbial species. We perform extensive simulation studies and show that our method allows for consistent estimation of global microbiome effects. Additionally, we investigate the performance of the model on two real-world microbiome studies: a study of microbiome-metabolome associations in inflammatory bowel disease, and a study of associations between diet and the gut microbiome in mice. We show that we can use the method to reliably detect associations in real-world datasets with varying numbers of samples and covariates.

Abstract Image

Abstract Image

Abstract Image

检测全球微生物组关联的分层贝叶斯方法。
人类肠道微生物组已被证明与多种人类疾病有关,包括癌症、代谢疾病和炎症性肠病。目前检测微生物组关联的方法受到限制,因为它们依赖于特定的生态距离测量,或者只能检测与单个细菌物种而非整个微生物组的关联。在这项工作中,我们开发了一种新型分层贝叶斯模型,用于检测全球微生物组关联。我们的方法不依赖于距离度量的选择,并且能够纳入微生物物种的系统发育信息。我们进行了大量的模拟研究,结果表明我们的方法可以对全球微生物组效应进行一致的估计。此外,我们还调查了该模型在两项实际微生物组研究中的表现:一项是炎症性肠病中微生物组-代谢组关联研究,另一项是小鼠饮食与肠道微生物组关联研究。我们的研究表明,我们可以用这种方法在样本数量和协变量各不相同的真实世界数据集中可靠地检测出相关性。
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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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