RT Journal Article
T1 A New Pipeline for the Normalization and Pooling of Metabolomics Data.
A1 Viallon, Vivian
A1 His, Mathilde
A1 Rinaldi, Sabina
A1 Breeur, Marie
A1 Gicquiau, Audrey
A1 Hemon, Bertrand
A1 Overvad, Kim
A1 Tjønneland, Anne
A1 Rostgaard-Hansen, Agnetha Linn
A1 Rothwell, Joseph A
A1 Lecuyer, Lucie
A1 Severi, Gianluca
A1 Kaaks, Rudolf
A1 Johnson, Theron
A1 Schulze, Matthias B
A1 Palli, Domenico
A1 Agnoli, Claudia
A1 Panico, Salvatore
A1 Tumino, Rosario
A1 Ricceri, Fulvio
A1 Verschuren, W M Monique
A1 Engelfriet, Peter
A1 Onland-Moret, Charlotte
A1 Vermeulen, Roel
A1 Nøst, Therese Haugdahl
A1 Urbarova, Ilona
A1 Zamora-Ros, Raul
A1 Rodriguez-Barranco, Miguel
A1 Amiano, Pilar
A1 Huerta, José Maria
A1 Ardanaz, Eva
A1 Melander, Olle
A1 Ottoson, Filip
A1 Vidman, Linda
A1 Rentoft, Matilda
A1 Schmidt, Julie A
A1 Travis, Ruth C
A1 Weiderpass, Elisabete
A1 Johansson, Mattias
A1 Dossus, Laure
A1 Jenab, Mazda
A1 Gunter, Marc J
A1 Lorenzo Bermejo, Justo
A1 Scherer, Dominique
A1 Salek, Reza M
A1 Keski-Rahkonen, Pekka
A1 Ferrari, Pietro
K1 cancer epidemiology
K1 metabolites
K1 metabolomics
K1 normalization
K1 pooling
K1 technical variability
AB 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.
SN 2218-1989
YR 2021
FD 2021-09-17
LK https://hdl.handle.net/10668/24707
UL https://hdl.handle.net/10668/24707
LA en
DS RISalud
RD Apr 7, 2025