Dataset Information

A New Pipeline for the Normalization and Pooling of Metabolomics Data.

ABSTRACT: Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples' originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.

SUBMITTER: Viallon V

PROVIDER: S-EPMC8467830 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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A New Pipeline for the Normalization and Pooling of Metabolomics Data.

Viallon Vivian V His Mathilde M Rinaldi Sabina S Breeur Marie M Gicquiau Audrey A Hemon Bertrand B Overvad Kim K Tjønneland Anne A Rostgaard-Hansen Agnetha Linn AL Rothwell Joseph A JA Lecuyer Lucie L Severi Gianluca G Kaaks Rudolf R Johnson Theron T Schulze Matthias B MB Palli Domenico D Agnoli Claudia C Panico Salvatore S Tumino Rosario R Ricceri Fulvio F Verschuren W M Monique WMM Engelfriet Peter P Onland-Moret Charlotte C Vermeulen Roel R Nøst Therese Haugdahl TH Urbarova Ilona I Zamora-Ros Raul R Rodriguez-Barranco Miguel M Amiano Pilar P Huerta José Maria JM Ardanaz Eva E Melander Olle O Ottoson Filip F Vidman Linda L Rentoft Matilda M Schmidt Julie A JA Travis Ruth C RC Weiderpass Elisabete E Johansson Mattias M Dossus Laure L Jenab Mazda M Gunter Marc J MJ Lorenzo Bermejo Justo J Scherer Dominique D Salek Reza M RM Keski-Rahkonen Pekka P Ferrari Pietro P

Metabolites 20210917 9

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using dif ...[more]

PMID: 34564446

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Project description:Metabolomics commonly relies on using one-dimensional (1D) 1H NMR spectroscopy or liquid chromatography-mass spectrometry (LC-MS) to derive scientific insights from large collections of biological samples. NMR and MS approaches to metabolomics require, among other issues, a data processing pipeline. Quantitative assessment of the performance of these software platforms is challenged by a lack of standardized data sets with "known" outcomes. To resolve this issue, we created a novel simulated LC-MS data set with known peak locations and intensities, defined metabolite differences between groups (i.e., fold change > 2, coefficient of variation ≤ 25%), and different amounts of added Gaussian noise (0, 5, or 10%) and missing features (0, 10, or 20%). This data set was developed to improve benchmarking of existing LC-MS metabolomics software and to validate the updated version of our MVAPACK software, which added gas chromatography-MS and LC-MS functionality to its existing 1D and two-dimensional NMR data processing capabilities. We also included two experimental LC-MS data sets acquired from a standard mixture andMycobacterium smegmatiscell lysates since a simulated data set alone may not capture all the unique characteristics and variability of real spectra needed to assess software performance properly. Our simulated and experimental LC-MS data sets were processed with the MS-DIAL and XCMSOnline software packages and our MVAPACK toolkit to showcase the utility of our data sets to benchmark MVAPACK against community standards. Our results demonstrate the enhanced objectivity and clarity of software assessment that can be achieved when both simulated and experimental data are employed since distinctly different software performances were observed with the simulated and experimental LC-MS data sets. We also demonstrate that the performance of MVAPACK is equivalent to or exceeds existing LC-MS software programs while providing a single platform for processing and analyzing both NMR and MS data sets.

Dataset Information

A New Pipeline for the Normalization and Pooling of Metabolomics Data.

Publications

A New Pipeline for the Normalization and Pooling of Metabolomics Data.

Similar Datasets

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