Empirical estimation of permutation-based metabolome-wide significance thresholds

File Description SizeFormat 
478370.full.pdfWorking paper435.47 kBAdobe PDFView/Open
Title: Empirical estimation of permutation-based metabolome-wide significance thresholds
Authors: Peluso, A
Ebbels, T
Glen, R
Item Type: Working Paper
Abstract: A key issue in the omics literature is the search of statistically significant relationships between molecular markers and phenotype. The aim is to detect disease-related discriminatory features while controlling for false positive associations at adequate power. Metabolome-wide association studies have revealed significant relationships of metabolic phenotypes with disease risk by analysing hundreds to tens of thousands of molecular variables leading to multivariate data which are highly noisy and collinear. In this context, Bonferroni or Sidak correction are rather useful as these are valid for independent tests, while permutation procedures allow for the estimation of p-values from the null distribution without assuming independence among features. Nevertheless, under the permutation approach the distribution of p-values may presents systematic deviations from the theoretical null distribution which leads to biased adjusted threshold estimate, e.g. smaller than a Bonferroni or Sidak correction. We make use of parametric approximation methods based on a multivariate Normal distribution to derive stable estimates of the metabolome-wide significance level within a univariate approach based on a permutation procedure which effectively controls the maximum overall type I error rate at the α level. We illustrate the results for different model parametrizations and distributional features of the outcome measure, as well as for diverse correlation levels within the features and between the features and the phenotype in real data and simulated studies. MWSL is the open-source R software package for the empirical estimation of the metabolomic-wide significance level available at
Issue Date: 27-Nov-2018
Publisher: bioRxiv
Copyright Statement: © 2018 The Author(s). This work is made available under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license (CC BY-NC-ND 4.0 -
Sponsor/Funder: European Molecular Biology Laboratory
National Institutes of Health
Funder's Grant Number: 654241
Publication Status: Published
Open Access location:
Appears in Collections:Division of Surgery

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commons