Normalization method for metabolomics data using optimal selection of multiple internal standards

<p>Abstract</p> <p>Background</p> <p>Success of metabolomics as the phenotyping platform largely depends on its ability to detect various sources of biological variability. Removal of platform-specific sources of variability such as systematic error is therefore one of...

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Main Authors: Yetukuri Laxman, Katajamaa Mikko, Sysi-Aho Marko, Orešič Matej
Format: Article
Language:English
Published: BMC 2007-03-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/8/93
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author Yetukuri Laxman
Katajamaa Mikko
Sysi-Aho Marko
Orešič Matej
author_facet Yetukuri Laxman
Katajamaa Mikko
Sysi-Aho Marko
Orešič Matej
author_sort Yetukuri Laxman
collection DOAJ
description <p>Abstract</p> <p>Background</p> <p>Success of metabolomics as the phenotyping platform largely depends on its ability to detect various sources of biological variability. Removal of platform-specific sources of variability such as systematic error is therefore one of the foremost priorities in data preprocessing. However, chemical diversity of molecular species included in typical metabolic profiling experiments leads to different responses to variations in experimental conditions, making normalization a very demanding task.</p> <p>Results</p> <p>With the aim to remove unwanted systematic variation, we present an approach that utilizes variability information from multiple internal standard compounds to find optimal normalization factor for each individual molecular species detected by metabolomics approach (NOMIS). We demonstrate the method on mouse liver lipidomic profiles using Ultra Performance Liquid Chromatography coupled to high resolution mass spectrometry, and compare its performance to two commonly utilized normalization methods: normalization by <it>l</it><sub>2 </sub>norm and by retention time region specific standard compound profiles. The NOMIS method proved superior in its ability to reduce the effect of systematic error across the full spectrum of metabolite peaks. We also demonstrate that the method can be used to select best combinations of standard compounds for normalization.</p> <p>Conclusion</p> <p>Depending on experiment design and biological matrix, the NOMIS method is applicable either as a one-step normalization method or as a two-step method where the normalization parameters, influenced by variabilities of internal standard compounds and their correlation to metabolites, are first calculated from a study conducted in repeatability conditions. The method can also be used in analytical development of metabolomics methods by helping to select best combinations of standard compounds for a particular biological matrix and analytical platform.</p>
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spelling doaj.art-0e08176ee87443a9af49ce3c8efb4c822022-12-22T02:04:28ZengBMCBMC Bioinformatics1471-21052007-03-01819310.1186/1471-2105-8-93Normalization method for metabolomics data using optimal selection of multiple internal standardsYetukuri LaxmanKatajamaa MikkoSysi-Aho MarkoOrešič Matej<p>Abstract</p> <p>Background</p> <p>Success of metabolomics as the phenotyping platform largely depends on its ability to detect various sources of biological variability. Removal of platform-specific sources of variability such as systematic error is therefore one of the foremost priorities in data preprocessing. However, chemical diversity of molecular species included in typical metabolic profiling experiments leads to different responses to variations in experimental conditions, making normalization a very demanding task.</p> <p>Results</p> <p>With the aim to remove unwanted systematic variation, we present an approach that utilizes variability information from multiple internal standard compounds to find optimal normalization factor for each individual molecular species detected by metabolomics approach (NOMIS). We demonstrate the method on mouse liver lipidomic profiles using Ultra Performance Liquid Chromatography coupled to high resolution mass spectrometry, and compare its performance to two commonly utilized normalization methods: normalization by <it>l</it><sub>2 </sub>norm and by retention time region specific standard compound profiles. The NOMIS method proved superior in its ability to reduce the effect of systematic error across the full spectrum of metabolite peaks. We also demonstrate that the method can be used to select best combinations of standard compounds for normalization.</p> <p>Conclusion</p> <p>Depending on experiment design and biological matrix, the NOMIS method is applicable either as a one-step normalization method or as a two-step method where the normalization parameters, influenced by variabilities of internal standard compounds and their correlation to metabolites, are first calculated from a study conducted in repeatability conditions. The method can also be used in analytical development of metabolomics methods by helping to select best combinations of standard compounds for a particular biological matrix and analytical platform.</p>http://www.biomedcentral.com/1471-2105/8/93
spellingShingle Yetukuri Laxman
Katajamaa Mikko
Sysi-Aho Marko
Orešič Matej
Normalization method for metabolomics data using optimal selection of multiple internal standards
BMC Bioinformatics
title Normalization method for metabolomics data using optimal selection of multiple internal standards
title_full Normalization method for metabolomics data using optimal selection of multiple internal standards
title_fullStr Normalization method for metabolomics data using optimal selection of multiple internal standards
title_full_unstemmed Normalization method for metabolomics data using optimal selection of multiple internal standards
title_short Normalization method for metabolomics data using optimal selection of multiple internal standards
title_sort normalization method for metabolomics data using optimal selection of multiple internal standards
url http://www.biomedcentral.com/1471-2105/8/93
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AT katajamaamikko normalizationmethodformetabolomicsdatausingoptimalselectionofmultipleinternalstandards
AT sysiahomarko normalizationmethodformetabolomicsdatausingoptimalselectionofmultipleinternalstandards
AT oresicmatej normalizationmethodformetabolomicsdatausingoptimalselectionofmultipleinternalstandards