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A new pipeline for the normalization and pooling of metabolomics data

Viallon, Vivian ; His, Mathilde ; Rinaldi, Sabina ; Breeur, Marie ; Gicquiau, Audrey ; Hemon, Bertrand ; Overvad, Kim ; Tjønneland, Anne ; Rostgaard-Hansen, Agnetha Linn and Rothwell, Joseph A. , et al. (2021) In Metabolites 11(9).
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;... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Cancer epidemiology, Metabolites, Metabolomics, Normalization, Pooling, Technical variability
in
Metabolites
volume
11
issue
9
article number
631
publisher
MDPI AG
external identifiers
  • scopus:85115861814
  • pmid:34564446
ISSN
2218-1989
DOI
10.3390/metabo11090631
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
id
13d41a4f-7812-4615-8ca3-76bee31b3125
date added to LUP
2021-10-14 13:46:33
date last changed
2024-12-15 13:55:50
@article{13d41a4f-7812-4615-8ca3-76bee31b3125,
  abstract     = {{<p>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.</p>}},
  author       = {{Viallon, Vivian and His, Mathilde and Rinaldi, Sabina and Breeur, Marie and Gicquiau, Audrey and Hemon, Bertrand and Overvad, Kim and Tjønneland, Anne and Rostgaard-Hansen, Agnetha Linn and Rothwell, Joseph A. and Lecuyer, Lucie and Severi, Gianluca and Kaaks, Rudolf and Johnson, Theron and Schulze, Matthias B. and Palli, Domenico and Agnoli, Claudia and Panico, Salvatore and Tumino, Rosario and Ricceri, Fulvio and Monique Verschuren, W. M. and Engelfriet, Peter and Onland-Moret, Charlotte and Vermeulen, Roel and Nøst, Therese Haugdahl and Urbarova, Ilona and Zamora-Ros, Raul and Rodriguez-Barranco, Miguel and Amiano, Pilar and Huerta, José Maria and Ardanaz, Eva and Melander, Olle and Ottoson, Filip and Vidman, Linda and Rentoft, Matilda and Schmidt, Julie A. and Travis, Ruth C. and Weiderpass, Elisabete and Johansson, Mattias and Dossus, Laure and Jenab, Mazda and Gunter, Marc J. and Bermejo, Justo Lorenzo and Scherer, Dominique and Salek, Reza M. and Keski-Rahkonen, Pekka and Ferrari, Pietro}},
  issn         = {{2218-1989}},
  keywords     = {{Cancer epidemiology; Metabolites; Metabolomics; Normalization; Pooling; Technical variability}},
  language     = {{eng}},
  number       = {{9}},
  publisher    = {{MDPI AG}},
  series       = {{Metabolites}},
  title        = {{A new pipeline for the normalization and pooling of metabolomics data}},
  url          = {{http://dx.doi.org/10.3390/metabo11090631}},
  doi          = {{10.3390/metabo11090631}},
  volume       = {{11}},
  year         = {{2021}},
}