Joint Microbial and Metabolomic Network Estimation with the Censored Gaussian Graphical Model
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Joint Microbial and Metabolomic Network Estimation with the Censored Gaussian Graphical Model Jing Ma1 Received: 15 March 2019 / Revised: 3 September 2020 / Accepted: 8 September 2020 © The Author(s) 2020
Abstract Joint analysis of microbiome and metabolomic data represents an imperative objective as the field moves beyond basic microbiome association studies and turns towards mechanistic and translational investigations. We present a censored Gaussian graphical model framework, where the metabolomic data are treated as continuous and the microbiome data as censored at zero, to identify direct interactions (defined as conditional dependence relationships) between microbial species and metabolites. Simulated examples show that our method metaMint performs favorably compared to the existing ones. metaMint also provides interpretable microbemetabolite interactions when applied to a bacterial vaginosis data set. R implementation of metaMint is available on GitHub. Keywords Data integration · Microbiome · Metabolomics · Censored Gaussian graphical models · Conditional dependence
1 Introduction The field of microbiome research is shifting rapidly from cataloging the taxonomic compositions of microbial communities [1] to refined technologies that capture strainlevel variations or amplicon sequence variants [2–4] and to multi-omics studies that better capture community functional activity [5]. In particular, metabolomics has been extremely useful in explaining microbial functional potential because of its capability in tracking microbially derived metabolites [6–8]. Associations between specific microbes and metabolites provide key insights and improved mechanistic models of host-microbe interactions [9–12]. In practice, the non-parametric Spearman’s rank correlation is often used to quantify the pairwise correlation between microbes and metabolites. However, Spearman’s rank correlation only captures marginal monotonic * Jing Ma [email protected] 1
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
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Vol.:(0123456789)
Statistics in Biosciences
association and does not distinguish direct and indirect interactions. In contrast, partial correlations measure conditional dependencies and allow the identification of direct interactions between microbes and metabolites [13]. One analytical challenge specific to the microbiome data are the uneven sequencing depths that arise due to differential efficiency of the sequencing process. The total number of reads in a sample is also constrained by the biological specimen at hand and does not reflect the absolute abundance present in the ecosystem. A common practice to address this issue is to transform the raw counts into relative abundances by normalizing over the total sequencing reads in each sample. In other words, raw sequencing counts are transformed into proportions of different microbes whose sum has to be one, also known as compositional data. Several lines of work have been proposed to model marginal and/or con
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