Cargando…
Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies
BACKGROUND: Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received li...
Autores principales: | , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6153696/ https://www.ncbi.nlm.nih.gov/pubmed/30830398 http://dx.doi.org/10.1007/s11306-018-1420-2 |
_version_ | 1783357554685902848 |
---|---|
author | Do, Kieu Trinh Wahl, Simone Raffler, Johannes Molnos, Sophie Laimighofer, Michael Adamski, Jerzy Suhre, Karsten Strauch, Konstantin Peters, Annette Gieger, Christian Langenberg, Claudia Stewart, Isobel D. Theis, Fabian J. Grallert, Harald Kastenmüller, Gabi Krumsiek, Jan |
author_facet | Do, Kieu Trinh Wahl, Simone Raffler, Johannes Molnos, Sophie Laimighofer, Michael Adamski, Jerzy Suhre, Karsten Strauch, Konstantin Peters, Annette Gieger, Christian Langenberg, Claudia Stewart, Isobel D. Theis, Fabian J. Grallert, Harald Kastenmüller, Gabi Krumsiek, Jan |
author_sort | Do, Kieu Trinh |
collection | PubMed |
description | BACKGROUND: Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation. METHODS: We investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n = 1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci. RESULTS: Run day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable. CONCLUSION: Missing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11306-018-1420-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6153696 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-61536962018-10-04 Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies Do, Kieu Trinh Wahl, Simone Raffler, Johannes Molnos, Sophie Laimighofer, Michael Adamski, Jerzy Suhre, Karsten Strauch, Konstantin Peters, Annette Gieger, Christian Langenberg, Claudia Stewart, Isobel D. Theis, Fabian J. Grallert, Harald Kastenmüller, Gabi Krumsiek, Jan Metabolomics Original Article BACKGROUND: Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation. METHODS: We investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n = 1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci. RESULTS: Run day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable. CONCLUSION: Missing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11306-018-1420-2) contains supplementary material, which is available to authorized users. Springer US 2018-09-20 2018 /pmc/articles/PMC6153696/ /pubmed/30830398 http://dx.doi.org/10.1007/s11306-018-1420-2 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article Do, Kieu Trinh Wahl, Simone Raffler, Johannes Molnos, Sophie Laimighofer, Michael Adamski, Jerzy Suhre, Karsten Strauch, Konstantin Peters, Annette Gieger, Christian Langenberg, Claudia Stewart, Isobel D. Theis, Fabian J. Grallert, Harald Kastenmüller, Gabi Krumsiek, Jan Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies |
title | Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies |
title_full | Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies |
title_fullStr | Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies |
title_full_unstemmed | Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies |
title_short | Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies |
title_sort | characterization of missing values in untargeted ms-based metabolomics data and evaluation of missing data handling strategies |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6153696/ https://www.ncbi.nlm.nih.gov/pubmed/30830398 http://dx.doi.org/10.1007/s11306-018-1420-2 |
work_keys_str_mv | AT dokieutrinh characterizationofmissingvaluesinuntargetedmsbasedmetabolomicsdataandevaluationofmissingdatahandlingstrategies AT wahlsimone characterizationofmissingvaluesinuntargetedmsbasedmetabolomicsdataandevaluationofmissingdatahandlingstrategies AT rafflerjohannes characterizationofmissingvaluesinuntargetedmsbasedmetabolomicsdataandevaluationofmissingdatahandlingstrategies AT molnossophie characterizationofmissingvaluesinuntargetedmsbasedmetabolomicsdataandevaluationofmissingdatahandlingstrategies AT laimighofermichael characterizationofmissingvaluesinuntargetedmsbasedmetabolomicsdataandevaluationofmissingdatahandlingstrategies AT adamskijerzy characterizationofmissingvaluesinuntargetedmsbasedmetabolomicsdataandevaluationofmissingdatahandlingstrategies AT suhrekarsten characterizationofmissingvaluesinuntargetedmsbasedmetabolomicsdataandevaluationofmissingdatahandlingstrategies AT strauchkonstantin characterizationofmissingvaluesinuntargetedmsbasedmetabolomicsdataandevaluationofmissingdatahandlingstrategies AT petersannette characterizationofmissingvaluesinuntargetedmsbasedmetabolomicsdataandevaluationofmissingdatahandlingstrategies AT giegerchristian characterizationofmissingvaluesinuntargetedmsbasedmetabolomicsdataandevaluationofmissingdatahandlingstrategies AT langenbergclaudia characterizationofmissingvaluesinuntargetedmsbasedmetabolomicsdataandevaluationofmissingdatahandlingstrategies AT stewartisobeld characterizationofmissingvaluesinuntargetedmsbasedmetabolomicsdataandevaluationofmissingdatahandlingstrategies AT theisfabianj characterizationofmissingvaluesinuntargetedmsbasedmetabolomicsdataandevaluationofmissingdatahandlingstrategies AT grallertharald characterizationofmissingvaluesinuntargetedmsbasedmetabolomicsdataandevaluationofmissingdatahandlingstrategies AT kastenmullergabi characterizationofmissingvaluesinuntargetedmsbasedmetabolomicsdataandevaluationofmissingdatahandlingstrategies AT krumsiekjan characterizationofmissingvaluesinuntargetedmsbasedmetabolomicsdataandevaluationofmissingdatahandlingstrategies |