Dataset Information

A new statistical workflow (R-packages based) to investigate associations between one variable of interest and the metabolome.

ABSTRACT:

Introduction

In metabolomics, the investigation of associations between the metabolome and one trait of interest is a key research question. However, statistical analyses of such associations are often challenging. Statistical tools enabling resilient verification and clear presentation are therefore highly desired.

Objectives

Our aim is to provide a contribution for statistical analysis of metabolomics data, offering a widely applicable open-source statistical workflow, which considers the intrinsic complexity of metabolomics data.

Methods

We combined selected R packages tailored for all properties of heterogeneous metabolomics datasets, where metabolite parameters typically (i) are analyzed in different matrices, (ii) are measured on different analytical platforms with different precision, (iii) are analyzed by targeted as well as non-targeted methods, (iv) are scaled variously, (v) reveal heterogeneous variances, (vi) may be correlated, (vii) may have only few values or values below a detection limit, or (viii) may be incomplete.

Results

The code is shared entirely and freely available. The workflow output is a table of metabolites associated with a trait of interest and a compact plot for high-quality results visualization. The workflow output and its utility are presented by applying it to two previously published datasets: one dataset from our own lab and another dataset taken from the repository MetaboLights.

Conclusion

Robustness and benefits of the statistical workflow were clearly demonstrated, and everyone can directly re-use it for analysis of own data.

SUBMITTER: Ferrario PG

PROVIDER: S-EPMC10689553 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Publications

A new statistical workflow (R-packages based) to investigate associations between one variable of interest and the metabolome.

Ferrario Paola G PG Bub Achim A Frommherz Lara L Krüger Ralf R Rist Manuela J MJ Watzl Bernhard B

Metabolomics : Official journal of the Metabolomic Society 20231130 1

<h4>Introduction</h4>In metabolomics, the investigation of associations between the metabolome and one trait of interest is a key research question. However, statistical analyses of such associations are often challenging. Statistical tools enabling resilient verification and clear presentation are therefore highly desired.<h4>Objectives</h4>Our aim is to provide a contribution for statistical analysis of metabolomics data, offering a widely applicable open-source statistical workflow, which con ...[more]

PMID: 38036896

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Project description:BackgroundLogistic random effects models are a popular tool to analyze multilevel also called hierarchical data with a binary or ordinal outcome. Here, we aim to compare different statistical software implementations of these models.MethodsWe used individual patient data from 8509 patients in 231 centers with moderate and severe Traumatic Brain Injury (TBI) enrolled in eight Randomized Controlled Trials (RCTs) and three observational studies. We fitted logistic random effects regression models with the 5-point Glasgow Outcome Scale (GOS) as outcome, both dichotomized as well as ordinal, with center and/or trial as random effects, and as covariates age, motor score, pupil reactivity or trial. We then compared the implementations of frequentist and Bayesian methods to estimate the fixed and random effects. Frequentist approaches included R (lme4), Stata (GLLAMM), SAS (GLIMMIX and NLMIXED), MLwiN ([R]IGLS) and MIXOR, Bayesian approaches included WinBUGS, MLwiN (MCMC), R package MCMCglmm and SAS experimental procedure MCMC.Three data sets (the full data set and two sub-datasets) were analysed using basically two logistic random effects models with either one random effect for the center or two random effects for center and trial. For the ordinal outcome in the full data set also a proportional odds model with a random center effect was fitted.ResultsThe packages gave similar parameter estimates for both the fixed and random effects and for the binary (and ordinal) models for the main study and when based on a relatively large number of level-1 (patient level) data compared to the number of level-2 (hospital level) data. However, when based on relatively sparse data set, i.e. when the numbers of level-1 and level-2 data units were about the same, the frequentist and Bayesian approaches showed somewhat different results. The software implementations differ considerably in flexibility, computation time, and usability. There are also differences in the availability of additional tools for model evaluation, such as diagnostic plots. The experimental SAS (version 9.2) procedure MCMC appeared to be inefficient.ConclusionsOn relatively large data sets, the different software implementations of logistic random effects regression models produced similar results. Thus, for a large data set there seems to be no explicit preference (of course if there is no preference from a philosophical point of view) for either a frequentist or Bayesian approach (if based on vague priors). The choice for a particular implementation may largely depend on the desired flexibility, and the usability of the package. For small data sets the random effects variances are difficult to estimate. In the frequentist approaches the MLE of this variance was often estimated zero with a standard error that is either zero or could not be determined, while for Bayesian methods the estimates could depend on the chosen "non-informative" prior of the variance parameter. The starting value for the variance parameter may be also critical for the convergence of the Markov chain.

Dataset Information

A new statistical workflow (R-packages based) to investigate associations between one variable of interest and the metabolome.

Introduction

Objectives

Methods

Results

Conclusion

Publications

A new statistical workflow (R-packages based) to investigate associations between one variable of interest and the metabolome.

Similar Datasets

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