Metabolomics data normalization with EigenMS
Liquid chromatography mass spectrometry has become one of the analytical platforms of choice for metabolomics studies. However, LC-MS metabolomics data can suffer from the effects of various systematic biases. These include batch effects, day-to-day variations in instrument performance, signal inten...
Gespeichert in:
| Veröffentlicht in: | PloS one 2014-12, Vol.9 (12), p.e116221-e116221 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | e116221 |
|---|---|
| container_issue | 12 |
| container_start_page | e116221 |
| container_title | PloS one |
| container_volume | 9 |
| creator | Karpievitch, Yuliya V Nikolic, Sonja B Wilson, Richard Sharman, James E Edwards, Lindsay M |
| description | Liquid chromatography mass spectrometry has become one of the analytical platforms of choice for metabolomics studies. However, LC-MS metabolomics data can suffer from the effects of various systematic biases. These include batch effects, day-to-day variations in instrument performance, signal intensity loss due to time-dependent effects of the LC column performance, accumulation of contaminants in the MS ion source and MS sensitivity among others. In this study we aimed to test a singular value decomposition-based method, called EigenMS, for normalization of metabolomics data. We analyzed a clinical human dataset where LC-MS serum metabolomics data and physiological measurements were collected from thirty nine healthy subjects and forty with type 2 diabetes and applied EigenMS to detect and correct for any systematic bias. EigenMS works in several stages. First, EigenMS preserves the treatment group differences in the metabolomics data by estimating treatment effects with an ANOVA model (multiple fixed effects can be estimated). Singular value decomposition of the residuals matrix is then used to determine bias trends in the data. The number of bias trends is then estimated via a permutation test and the effects of the bias trends are eliminated. EigenMS removed bias of unknown complexity from the LC-MS metabolomics data, allowing for increased sensitivity in differential analysis. Moreover, normalized samples better correlated with both other normalized samples and corresponding physiological data, such as blood glucose level, glycated haemoglobin, exercise central augmentation pressure normalized to heart rate of 75, and total cholesterol. We were able to report 2578 discriminatory metabolite peaks in the normalized data (p |
| doi_str_mv | 10.1371/journal.pone.0116221 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_1641028983</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A417148499</galeid><doaj_id>oai_doaj_org_article_00bd0d74a0a14e30be216e230e82216f</doaj_id><sourcerecordid>A417148499</sourcerecordid><originalsourceid>FETCH-LOGICAL-c692t-b32c4934c5057bac45eae993fed6f8889a37c6a5a6d2ee58a5628d6ae500b5173</originalsourceid><addsrcrecordid>eNqNkl1rFDEUhgdRbF39B6ILglhw13xP5kYopdaFloJVb8OZmTO7WbKTdZLx69ebdadlR3ohuUhInvPmfLxZ9pySOeU5fbf2fdeCm299i3NCqWKMPsiOacHZTDHCHx6cj7InIawJkVwr9Tg7YlKKgmh-nL29wgild35jqzCtIcK09d0GnP0N0fp2-sPG1fTcLrG9unmaPWrABXw27JPsy4fzz2cfZ5fXF4uz08tZpQoWZyVnlSi4qCSReQmVkAhYFLzBWjVa6wJ4XimQoGqGKDVIxXStACUhpaQ5n2Qv97pb54MZCg2GKkEJ04XmiVjsidrD2mw7u4Hul_Fgzd8L3y0NdNFWDk3SrEmdCyBABXJSIqMKGSeoU8dUk7TeD7_15QbrCtvYgRuJjl9auzJL_90IpgkVu2TeDAKd_9ZjiGZjQ4XOQYu-3-ctmCJUJvTVP-j91Q3UElIBtm18-rfaiZpTQXMqtEjtnGTze6i0akzDTK5obLofBZyMAhIT8WdcQh-CWdx8-n_2-uuYfX3ArhBcXAXv-p1_whgUe7DqfAgdNndNpsTsTH3bDbMztRlMncJeHA7oLujWxfwP-5_veg</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1641028983</pqid></control><display><type>article</type><title>Metabolomics data normalization with EigenMS</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Public Library of Science (PLoS)</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Karpievitch, Yuliya V ; Nikolic, Sonja B ; Wilson, Richard ; Sharman, James E ; Edwards, Lindsay M</creator><contributor>Dalby, Andrew R.</contributor><creatorcontrib>Karpievitch, Yuliya V ; Nikolic, Sonja B ; Wilson, Richard ; Sharman, James E ; Edwards, Lindsay M ; Dalby, Andrew R.</creatorcontrib><description>Liquid chromatography mass spectrometry has become one of the analytical platforms of choice for metabolomics studies. However, LC-MS metabolomics data can suffer from the effects of various systematic biases. These include batch effects, day-to-day variations in instrument performance, signal intensity loss due to time-dependent effects of the LC column performance, accumulation of contaminants in the MS ion source and MS sensitivity among others. In this study we aimed to test a singular value decomposition-based method, called EigenMS, for normalization of metabolomics data. We analyzed a clinical human dataset where LC-MS serum metabolomics data and physiological measurements were collected from thirty nine healthy subjects and forty with type 2 diabetes and applied EigenMS to detect and correct for any systematic bias. EigenMS works in several stages. First, EigenMS preserves the treatment group differences in the metabolomics data by estimating treatment effects with an ANOVA model (multiple fixed effects can be estimated). Singular value decomposition of the residuals matrix is then used to determine bias trends in the data. The number of bias trends is then estimated via a permutation test and the effects of the bias trends are eliminated. EigenMS removed bias of unknown complexity from the LC-MS metabolomics data, allowing for increased sensitivity in differential analysis. Moreover, normalized samples better correlated with both other normalized samples and corresponding physiological data, such as blood glucose level, glycated haemoglobin, exercise central augmentation pressure normalized to heart rate of 75, and total cholesterol. We were able to report 2578 discriminatory metabolite peaks in the normalized data (p<0.05) as compared to only 1840 metabolite signals in the raw data. Our results support the use of singular value decomposition-based normalization for metabolomics data.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0116221</identifier><identifier>PMID: 25549083</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Aged ; Bias ; Biology and Life Sciences ; Blood sugar ; Cholesterol ; Chromatography ; Chromatography, Liquid - methods ; Contaminants ; Correlation analysis ; Data processing ; Databases, Factual ; Decomposition ; Diabetes ; Diabetes mellitus ; Diabetes mellitus (non-insulin dependent) ; Diabetes Mellitus, Type 2 - blood ; Diabetes Mellitus, Type 2 - metabolism ; Experiments ; Gene expression ; Heart rate ; Hemoglobin ; Humans ; Laboratories ; Liquid chromatography ; Mass spectrometry ; Mass Spectrometry - methods ; Mass spectroscopy ; Metabolism ; Metabolites ; Metabolomics ; Metabolomics - methods ; Methods ; Middle Aged ; Permutations ; Physical Sciences ; Physiology ; Proteomics ; Reproducibility of Results ; Research and Analysis Methods ; Scientific imaging ; Sensitivity ; Sensitivity analysis ; Singular value decomposition ; Software ; Studies ; Time dependence ; Trends ; Urine ; Variance analysis</subject><ispartof>PloS one, 2014-12, Vol.9 (12), p.e116221-e116221</ispartof><rights>COPYRIGHT 2014 Public Library of Science</rights><rights>2014 Karpievitch et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2014 Karpievitch et al 2014 Karpievitch et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-b32c4934c5057bac45eae993fed6f8889a37c6a5a6d2ee58a5628d6ae500b5173</citedby><cites>FETCH-LOGICAL-c692t-b32c4934c5057bac45eae993fed6f8889a37c6a5a6d2ee58a5628d6ae500b5173</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4280143/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4280143/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25549083$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Dalby, Andrew R.</contributor><creatorcontrib>Karpievitch, Yuliya V</creatorcontrib><creatorcontrib>Nikolic, Sonja B</creatorcontrib><creatorcontrib>Wilson, Richard</creatorcontrib><creatorcontrib>Sharman, James E</creatorcontrib><creatorcontrib>Edwards, Lindsay M</creatorcontrib><title>Metabolomics data normalization with EigenMS</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Liquid chromatography mass spectrometry has become one of the analytical platforms of choice for metabolomics studies. However, LC-MS metabolomics data can suffer from the effects of various systematic biases. These include batch effects, day-to-day variations in instrument performance, signal intensity loss due to time-dependent effects of the LC column performance, accumulation of contaminants in the MS ion source and MS sensitivity among others. In this study we aimed to test a singular value decomposition-based method, called EigenMS, for normalization of metabolomics data. We analyzed a clinical human dataset where LC-MS serum metabolomics data and physiological measurements were collected from thirty nine healthy subjects and forty with type 2 diabetes and applied EigenMS to detect and correct for any systematic bias. EigenMS works in several stages. First, EigenMS preserves the treatment group differences in the metabolomics data by estimating treatment effects with an ANOVA model (multiple fixed effects can be estimated). Singular value decomposition of the residuals matrix is then used to determine bias trends in the data. The number of bias trends is then estimated via a permutation test and the effects of the bias trends are eliminated. EigenMS removed bias of unknown complexity from the LC-MS metabolomics data, allowing for increased sensitivity in differential analysis. Moreover, normalized samples better correlated with both other normalized samples and corresponding physiological data, such as blood glucose level, glycated haemoglobin, exercise central augmentation pressure normalized to heart rate of 75, and total cholesterol. We were able to report 2578 discriminatory metabolite peaks in the normalized data (p<0.05) as compared to only 1840 metabolite signals in the raw data. Our results support the use of singular value decomposition-based normalization for metabolomics data.</description><subject>Aged</subject><subject>Bias</subject><subject>Biology and Life Sciences</subject><subject>Blood sugar</subject><subject>Cholesterol</subject><subject>Chromatography</subject><subject>Chromatography, Liquid - methods</subject><subject>Contaminants</subject><subject>Correlation analysis</subject><subject>Data processing</subject><subject>Databases, Factual</subject><subject>Decomposition</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Diabetes Mellitus, Type 2 - blood</subject><subject>Diabetes Mellitus, Type 2 - metabolism</subject><subject>Experiments</subject><subject>Gene expression</subject><subject>Heart rate</subject><subject>Hemoglobin</subject><subject>Humans</subject><subject>Laboratories</subject><subject>Liquid chromatography</subject><subject>Mass spectrometry</subject><subject>Mass Spectrometry - methods</subject><subject>Mass spectroscopy</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Metabolomics</subject><subject>Metabolomics - methods</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Permutations</subject><subject>Physical Sciences</subject><subject>Physiology</subject><subject>Proteomics</subject><subject>Reproducibility of Results</subject><subject>Research and Analysis Methods</subject><subject>Scientific imaging</subject><subject>Sensitivity</subject><subject>Sensitivity analysis</subject><subject>Singular value decomposition</subject><subject>Software</subject><subject>Studies</subject><subject>Time dependence</subject><subject>Trends</subject><subject>Urine</subject><subject>Variance analysis</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNkl1rFDEUhgdRbF39B6ILglhw13xP5kYopdaFloJVb8OZmTO7WbKTdZLx69ebdadlR3ohuUhInvPmfLxZ9pySOeU5fbf2fdeCm299i3NCqWKMPsiOacHZTDHCHx6cj7InIawJkVwr9Tg7YlKKgmh-nL29wgild35jqzCtIcK09d0GnP0N0fp2-sPG1fTcLrG9unmaPWrABXw27JPsy4fzz2cfZ5fXF4uz08tZpQoWZyVnlSi4qCSReQmVkAhYFLzBWjVa6wJ4XimQoGqGKDVIxXStACUhpaQ5n2Qv97pb54MZCg2GKkEJ04XmiVjsidrD2mw7u4Hul_Fgzd8L3y0NdNFWDk3SrEmdCyBABXJSIqMKGSeoU8dUk7TeD7_15QbrCtvYgRuJjl9auzJL_90IpgkVu2TeDAKd_9ZjiGZjQ4XOQYu-3-ctmCJUJvTVP-j91Q3UElIBtm18-rfaiZpTQXMqtEjtnGTze6i0akzDTK5obLofBZyMAhIT8WdcQh-CWdx8-n_2-uuYfX3ArhBcXAXv-p1_whgUe7DqfAgdNndNpsTsTH3bDbMztRlMncJeHA7oLujWxfwP-5_veg</recordid><startdate>20141230</startdate><enddate>20141230</enddate><creator>Karpievitch, Yuliya V</creator><creator>Nikolic, Sonja B</creator><creator>Wilson, Richard</creator><creator>Sharman, James E</creator><creator>Edwards, Lindsay M</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20141230</creationdate><title>Metabolomics data normalization with EigenMS</title><author>Karpievitch, Yuliya V ; Nikolic, Sonja B ; Wilson, Richard ; Sharman, James E ; Edwards, Lindsay M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-b32c4934c5057bac45eae993fed6f8889a37c6a5a6d2ee58a5628d6ae500b5173</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Aged</topic><topic>Bias</topic><topic>Biology and Life Sciences</topic><topic>Blood sugar</topic><topic>Cholesterol</topic><topic>Chromatography</topic><topic>Chromatography, Liquid - methods</topic><topic>Contaminants</topic><topic>Correlation analysis</topic><topic>Data processing</topic><topic>Databases, Factual</topic><topic>Decomposition</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetes mellitus (non-insulin dependent)</topic><topic>Diabetes Mellitus, Type 2 - blood</topic><topic>Diabetes Mellitus, Type 2 - metabolism</topic><topic>Experiments</topic><topic>Gene expression</topic><topic>Heart rate</topic><topic>Hemoglobin</topic><topic>Humans</topic><topic>Laboratories</topic><topic>Liquid chromatography</topic><topic>Mass spectrometry</topic><topic>Mass Spectrometry - methods</topic><topic>Mass spectroscopy</topic><topic>Metabolism</topic><topic>Metabolites</topic><topic>Metabolomics</topic><topic>Metabolomics - methods</topic><topic>Methods</topic><topic>Middle Aged</topic><topic>Permutations</topic><topic>Physical Sciences</topic><topic>Physiology</topic><topic>Proteomics</topic><topic>Reproducibility of Results</topic><topic>Research and Analysis Methods</topic><topic>Scientific imaging</topic><topic>Sensitivity</topic><topic>Sensitivity analysis</topic><topic>Singular value decomposition</topic><topic>Software</topic><topic>Studies</topic><topic>Time dependence</topic><topic>Trends</topic><topic>Urine</topic><topic>Variance analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Karpievitch, Yuliya V</creatorcontrib><creatorcontrib>Nikolic, Sonja B</creatorcontrib><creatorcontrib>Wilson, Richard</creatorcontrib><creatorcontrib>Sharman, James E</creatorcontrib><creatorcontrib>Edwards, Lindsay M</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Karpievitch, Yuliya V</au><au>Nikolic, Sonja B</au><au>Wilson, Richard</au><au>Sharman, James E</au><au>Edwards, Lindsay M</au><au>Dalby, Andrew R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Metabolomics data normalization with EigenMS</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2014-12-30</date><risdate>2014</risdate><volume>9</volume><issue>12</issue><spage>e116221</spage><epage>e116221</epage><pages>e116221-e116221</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Liquid chromatography mass spectrometry has become one of the analytical platforms of choice for metabolomics studies. However, LC-MS metabolomics data can suffer from the effects of various systematic biases. These include batch effects, day-to-day variations in instrument performance, signal intensity loss due to time-dependent effects of the LC column performance, accumulation of contaminants in the MS ion source and MS sensitivity among others. In this study we aimed to test a singular value decomposition-based method, called EigenMS, for normalization of metabolomics data. We analyzed a clinical human dataset where LC-MS serum metabolomics data and physiological measurements were collected from thirty nine healthy subjects and forty with type 2 diabetes and applied EigenMS to detect and correct for any systematic bias. EigenMS works in several stages. First, EigenMS preserves the treatment group differences in the metabolomics data by estimating treatment effects with an ANOVA model (multiple fixed effects can be estimated). Singular value decomposition of the residuals matrix is then used to determine bias trends in the data. The number of bias trends is then estimated via a permutation test and the effects of the bias trends are eliminated. EigenMS removed bias of unknown complexity from the LC-MS metabolomics data, allowing for increased sensitivity in differential analysis. Moreover, normalized samples better correlated with both other normalized samples and corresponding physiological data, such as blood glucose level, glycated haemoglobin, exercise central augmentation pressure normalized to heart rate of 75, and total cholesterol. We were able to report 2578 discriminatory metabolite peaks in the normalized data (p<0.05) as compared to only 1840 metabolite signals in the raw data. Our results support the use of singular value decomposition-based normalization for metabolomics data.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>25549083</pmid><doi>10.1371/journal.pone.0116221</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2014-12, Vol.9 (12), p.e116221-e116221 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1641028983 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Aged Bias Biology and Life Sciences Blood sugar Cholesterol Chromatography Chromatography, Liquid - methods Contaminants Correlation analysis Data processing Databases, Factual Decomposition Diabetes Diabetes mellitus Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 - blood Diabetes Mellitus, Type 2 - metabolism Experiments Gene expression Heart rate Hemoglobin Humans Laboratories Liquid chromatography Mass spectrometry Mass Spectrometry - methods Mass spectroscopy Metabolism Metabolites Metabolomics Metabolomics - methods Methods Middle Aged Permutations Physical Sciences Physiology Proteomics Reproducibility of Results Research and Analysis Methods Scientific imaging Sensitivity Sensitivity analysis Singular value decomposition Software Studies Time dependence Trends Urine Variance analysis |
| title | Metabolomics data normalization with EigenMS |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T11%3A47%3A10IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Metabolomics%20data%20normalization%20with%20EigenMS&rft.jtitle=PloS%20one&rft.au=Karpievitch,%20Yuliya%20V&rft.date=2014-12-30&rft.volume=9&rft.issue=12&rft.spage=e116221&rft.epage=e116221&rft.pages=e116221-e116221&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0116221&rft_dat=%3Cgale_plos_%3EA417148499%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1641028983&rft_id=info:pmid/25549083&rft_galeid=A417148499&rft_doaj_id=oai_doaj_org_article_00bd0d74a0a14e30be216e230e82216f&rfr_iscdi=true |