A New Pipeline for the Normalization and Pooling of Metabolomics Data
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. Sp...
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Format: | Article |
Language: | English |
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MDPI AG
2021-09-01
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Series: | Metabolites |
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Online Access: | https://www.mdpi.com/2218-1989/11/9/631 |
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language |
English |
format |
Article |
sources |
DOAJ |
author |
Vivian Viallon Mathilde His Sabina Rinaldi Marie Breeur Audrey Gicquiau Bertrand Hemon Kim Overvad Anne Tjønneland Agnetha Linn Rostgaard-Hansen Joseph A. Rothwell Lucie Lecuyer Gianluca Severi Rudolf Kaaks Theron Johnson Matthias B. Schulze Domenico Palli Claudia Agnoli Salvatore Panico Rosario Tumino Fulvio Ricceri W. M. Monique Verschuren Peter Engelfriet Charlotte Onland-Moret Roel Vermeulen Therese Haugdahl Nøst Ilona Urbarova Raul Zamora-Ros Miguel Rodriguez-Barranco Pilar Amiano José Maria Huerta Eva Ardanaz Olle Melander Filip Ottoson Linda Vidman Matilda Rentoft Julie A. Schmidt Ruth C. Travis Elisabete Weiderpass Mattias Johansson Laure Dossus Mazda Jenab Marc J. Gunter Justo Lorenzo Bermejo Dominique Scherer Reza M. Salek Pekka Keski-Rahkonen Pietro Ferrari |
spellingShingle |
Vivian Viallon Mathilde His Sabina Rinaldi Marie Breeur Audrey Gicquiau Bertrand Hemon Kim Overvad Anne Tjønneland Agnetha Linn Rostgaard-Hansen Joseph A. Rothwell Lucie Lecuyer Gianluca Severi Rudolf Kaaks Theron Johnson Matthias B. Schulze Domenico Palli Claudia Agnoli Salvatore Panico Rosario Tumino Fulvio Ricceri W. M. Monique Verschuren Peter Engelfriet Charlotte Onland-Moret Roel Vermeulen Therese Haugdahl Nøst Ilona Urbarova Raul Zamora-Ros Miguel Rodriguez-Barranco Pilar Amiano José Maria Huerta Eva Ardanaz Olle Melander Filip Ottoson Linda Vidman Matilda Rentoft Julie A. Schmidt Ruth C. Travis Elisabete Weiderpass Mattias Johansson Laure Dossus Mazda Jenab Marc J. Gunter Justo Lorenzo Bermejo Dominique Scherer Reza M. Salek Pekka Keski-Rahkonen Pietro Ferrari A New Pipeline for the Normalization and Pooling of Metabolomics Data Metabolites cancer epidemiology normalization pooling technical variability metabolomics metabolites |
author_facet |
Vivian Viallon Mathilde His Sabina Rinaldi Marie Breeur Audrey Gicquiau Bertrand Hemon Kim Overvad Anne Tjønneland Agnetha Linn Rostgaard-Hansen Joseph A. Rothwell Lucie Lecuyer Gianluca Severi Rudolf Kaaks Theron Johnson Matthias B. Schulze Domenico Palli Claudia Agnoli Salvatore Panico Rosario Tumino Fulvio Ricceri W. M. Monique Verschuren Peter Engelfriet Charlotte Onland-Moret Roel Vermeulen Therese Haugdahl Nøst Ilona Urbarova Raul Zamora-Ros Miguel Rodriguez-Barranco Pilar Amiano José Maria Huerta Eva Ardanaz Olle Melander Filip Ottoson Linda Vidman Matilda Rentoft Julie A. Schmidt Ruth C. Travis Elisabete Weiderpass Mattias Johansson Laure Dossus Mazda Jenab Marc J. Gunter Justo Lorenzo Bermejo Dominique Scherer Reza M. Salek Pekka Keski-Rahkonen Pietro Ferrari |
author_sort |
Vivian Viallon |
title |
A New Pipeline for the Normalization and Pooling of Metabolomics Data |
title_short |
A New Pipeline for the Normalization and Pooling of Metabolomics Data |
title_full |
A New Pipeline for the Normalization and Pooling of Metabolomics Data |
title_fullStr |
A New Pipeline for the Normalization and Pooling of Metabolomics Data |
title_full_unstemmed |
A New Pipeline for the Normalization and Pooling of Metabolomics Data |
title_sort |
new pipeline for the normalization and pooling of metabolomics data |
publisher |
MDPI AG |
series |
Metabolites |
issn |
2218-1989 |
publishDate |
2021-09-01 |
description |
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. |
topic |
cancer epidemiology normalization pooling technical variability metabolomics metabolites |
url |
https://www.mdpi.com/2218-1989/11/9/631 |
work_keys_str_mv |
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doaj-5d0c8a10e0e24f48994bc80dce1817042021-09-26T00:41:02ZengMDPI AGMetabolites2218-19892021-09-011163163110.3390/metabo11090631A New Pipeline for the Normalization and Pooling of Metabolomics DataVivian Viallon0Mathilde His1Sabina Rinaldi2Marie Breeur3Audrey Gicquiau4Bertrand Hemon5Kim Overvad6Anne Tjønneland7Agnetha Linn Rostgaard-Hansen8Joseph A. Rothwell9Lucie Lecuyer10Gianluca Severi11Rudolf Kaaks12Theron Johnson13Matthias B. Schulze14Domenico Palli15Claudia Agnoli16Salvatore Panico17Rosario Tumino18Fulvio Ricceri19W. M. Monique Verschuren20Peter Engelfriet21Charlotte Onland-Moret22Roel Vermeulen23Therese Haugdahl Nøst24Ilona Urbarova25Raul Zamora-Ros26Miguel Rodriguez-Barranco27Pilar Amiano28José Maria Huerta29Eva Ardanaz30Olle Melander31Filip Ottoson32Linda Vidman33Matilda Rentoft34Julie A. Schmidt35Ruth C. Travis36Elisabete Weiderpass37Mattias Johansson38Laure Dossus39Mazda Jenab40Marc J. Gunter41Justo Lorenzo Bermejo42Dominique Scherer43Reza M. Salek44Pekka Keski-Rahkonen45Pietro Ferrari46Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, FranceNutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, FranceNutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, FranceNutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, FranceNutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, FranceNutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, FranceDepartment of Public Health, Aarhus University Bartholins Alle 2, DK-8000 Aarhus, DenmarkDanish Cancer Society Research Center, DK-2100 Copenhagen, DenmarkDanish Cancer Society Research Center, DK-2100 Copenhagen, DenmarkUVSQ, Inserm, CESP U1018, “Exposome and Heredity” Team, Université Paris-Saclay, Gustave Roussy, 94800 Villejuif, FranceUVSQ, Inserm, CESP U1018, “Exposome and Heredity” Team, Université Paris-Saclay, Gustave Roussy, 94800 Villejuif, FranceUVSQ, Inserm, CESP U1018, “Exposome and Heredity” Team, Université Paris-Saclay, Gustave Roussy, 94800 Villejuif, FranceDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, GermanyDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, GermanyDepartment of Molecular Epidemiology, German Institute of Human Nutrition Potsdam Rehbruecke, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, GermanyCancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50139 Florence, ItalyEpidemiology and Prevention Unit Department of Research, Fondazione IRCCS—Istituto Nazionale dei Tumori, 20133 Milan, ItalyDipartimento di Medicina Clinica e Chirurgia, Federico II University, 80131 Naples, ItalyCancer Registry and Histopathology Department, Provincial Health Authority (ASP 7), 97100 Ragusa, ItalyDepartment of Clinical and Biological Sciences, University of Turin, 10043 Orbassano, ItalyNational Institute for Public Health and the Environment, Centre for Nutrition, Prevention and Health Services, Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, The NetherlandsNational Institute for Public Health and the Environment, Centre for Nutrition, Prevention and Health Services, Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, The NetherlandsJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 CG Utrecht, The NetherlandsJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 CG Utrecht, The NetherlandsDepartment of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, P.O. Box 6050, 9037 Tromsø, NorwayDepartment of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, P.O. Box 6050, 9037 Tromsø, NorwayUnit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L’Hospitalet de Llobregat, SpainEscuela Andaluza de Salud Pública (EASP), 18011 Granada, SpainCentro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, SpainCentro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, SpainCentro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, SpainDepartment of Clincal Sciences, Lund University, SE-21 428 Malmö, SwedenDepartment of Immunotechnology, Lund University, SE-22 100 Lund, SwedenDepartment of Radiation Sciences, Oncology, Umeå University, SE-901 87 Umeå, SwedenDepartment of Radiation Sciences, Oncology, Umeå University, SE-901 87 Umeå, SwedenCancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UKCancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UKInternational Agency for Research on Cancer, World Health Organization, 69008 Lyon, FranceGenomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, FranceNutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, FranceNutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, FranceNutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, FranceStatistical Genetics Group, Institute of Medical Biometry, University of Heidelberg, 69120 Heidelberg, GermanyStatistical Genetics Group, Institute of Medical Biometry, University of Heidelberg, 69120 Heidelberg, GermanyNutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, FranceNutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, FranceNutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, FrancePooling 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.https://www.mdpi.com/2218-1989/11/9/631cancer epidemiologynormalizationpoolingtechnical variabilitymetabolomicsmetabolites |