{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T23:14:25Z","timestamp":1772234065858,"version":"3.50.1"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2007,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats: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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats: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<jats:italic>l<\/jats:italic><jats:sub>2<\/jats: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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats: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.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1471-2105-8-93","type":"journal-article","created":{"date-parts":[[2007,5,2]],"date-time":"2007-05-02T15:30:27Z","timestamp":1178119827000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":303,"title":["Normalization method for metabolomics data using optimal selection of multiple internal standards"],"prefix":"10.1186","volume":"8","author":[{"given":"Marko","family":"Sysi-Aho","sequence":"first","affiliation":[]},{"given":"Mikko","family":"Katajamaa","sequence":"additional","affiliation":[]},{"given":"Laxman","family":"Yetukuri","sequence":"additional","affiliation":[]},{"given":"Matej","family":"Ore\u0161i\u010d","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2007,3,15]]},"reference":[{"key":"1465_CR1","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1038\/83496","volume":"19","author":"LM Raamsdonk","year":"2001","unstructured":"Raamsdonk LM, Teusink B, Broadhurst D, Zhang N, Hayes A, Walsh MC, Berden JA, Brindle KM, Kell DB, Rowland JJ, Westerhoff HV, van Dam K, Oliver SG: A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat Biotech 2001, 19: 45\u201350. 10.1038\/83496","journal-title":"Nat Biotech"},{"key":"1465_CR2","doi-asserted-by":"publisher","first-page":"205","DOI":"10.2165\/00822942-200403040-00002","volume":"3","author":"M Oresic","year":"2004","unstructured":"Oresic M, Clish CB, Davidov EJ, Verheij E, Vogels JTWE, Havekes LM, Neumann E, Adourian A, Naylor S, Greef J, Plasterer T: Phenotype characterization using integrated gene transcript, protein and metabolite profiling. Appl Bioinformatics 2004, 3: 205\u2013217. 10.2165\/00822942-200403040-00002","journal-title":"Appl Bioinformatics"},{"key":"1465_CR3","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1586\/14737159.6.4.575","volume":"6","author":"M Oresic","year":"2006","unstructured":"Oresic M, Vidal-Puig A, Hanninen V: Metabolomic approaches to phenotype characterization and applications to complex diseases. Expert Rev Mol Diagn 2006, 6: 575\u2013585. 10.1586\/14737159.6.4.575","journal-title":"Expert Rev Mol Diagn"},{"key":"1465_CR4","doi-asserted-by":"publisher","first-page":"2374","DOI":"10.1073\/pnas.68.10.2374","volume":"68","author":"L Pauling","year":"1971","unstructured":"Pauling L, Robinson AB, Teranishi R, Cary P: Quantitative analysis of urine vapor and breath by gas-liquid partition chromatography. Proc Nat Acad Sci U S A 1971, 68: 2374\u20132376. 10.1073\/pnas.68.10.2374","journal-title":"Proc Nat Acad Sci U S A"},{"key":"1465_CR5","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/978-1-4615-0333-0_10","volume-title":"Metabolic profiling: Its role in biomarker discovery and gene function analysis","author":"J van der Greef","year":"2003","unstructured":"van der Greef J, Davidov E, Verheij E, Vogels JTWE, van der Heijden R, Adourian AS, Oresic M, Marple EW, Naylor S: The role of metabolomics in systems biology: A new vision for drug discovery and development. In Metabolic profiling: Its role in biomarker discovery and gene function analysis. Edited by: Harrigan GG and Goodacre R. Boston, Mass., Kluwer Academic Publishers; 2003:171\u2013198."},{"key":"1465_CR6","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1016\/j.cbpa.2004.08.013","volume":"8","author":"J van der Greef","year":"2004","unstructured":"van der Greef J, Stroobant P, Heijden R: The role of analytical sciences in medical systems biology. Curr Opin Chem Biol 2004, 8: 559\u2013565. 10.1016\/j.cbpa.2004.08.013","journal-title":"Curr Opin Chem Biol"},{"key":"1465_CR7","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.tibtech.2004.03.007","volume":"22","author":"R Goodacre","year":"2004","unstructured":"Goodacre R, Vaidyanathan S, Dunn WB, Harrigan GG, Kell DB: Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol 2004, 22: 245\u2013252. 10.1016\/j.tibtech.2004.03.007","journal-title":"Trends Biotechnol"},{"key":"1465_CR8","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1186\/1471-2164-7-142","volume":"7","author":"RA van den Berg","year":"2006","unstructured":"van den Berg RA, Hoefsloot HC, Westerhuis JA, Smilde AK, van der Werf MJ: Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 2006, 7: 142. 10.1186\/1471-2164-7-142","journal-title":"BMC Genomics"},{"key":"1465_CR9","volume-title":"Mass spectrometry: Principles and applications","author":"E de Hoffmann","year":"2001","unstructured":"de Hoffmann E, Stroobant V: Mass spectrometry: Principles and applications. 2.th edition. , John Wiley & Sons; 2001.","edition":"2."},{"key":"1465_CR10","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1021\/ac60266a027","volume":"40","author":"LR Crawford","year":"1968","unstructured":"Crawford LR, Morrison JD: Computer methods in analytical mass spectrometry. Identification of an unknown compound in a catalog. Anal Chem 1968, 40: 1464\u20131469. 10.1021\/ac60266a027","journal-title":"Anal Chem"},{"key":"1465_CR11","doi-asserted-by":"publisher","first-page":"2447","DOI":"10.1093\/bioinformatics\/bth270","volume":"20","author":"M Scholz","year":"2004","unstructured":"Scholz M, Gatzek S, Sterling A, Fiehn O, Selbig J: Metabolite fingerprinting: detecting biological features by independent component analysis. Bioinformatics 2004, 20: 2447\u20132454. 10.1093\/bioinformatics\/bth270","journal-title":"Bioinformatics"},{"key":"1465_CR12","doi-asserted-by":"publisher","first-page":"4818","DOI":"10.1021\/ac026468x","volume":"75","author":"W Wang","year":"2003","unstructured":"Wang W, Zhou H, Lin H, Roy S, Shaler TA, Hill LR, Norton S, Kumar P, Anderle M, Becker CH: Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. Anal Chem 2003, 75: 4818 -44826. 10.1021\/ac026468x","journal-title":"Anal Chem"},{"key":"1465_CR13","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1117\/12.427981","volume-title":"Microarrays: optical technologies and informatics Proceedings of SPIE (vol 4266)","author":"AJ Hartemink","year":"2001","unstructured":"Hartemink AJ, Gifford DK, Jaakkola TS, Young RA: Maximum likelihood estimation of optimal scaling factors for expression array normalization. In Microarrays: optical technologies and informatics Proceedings of SPIE (vol 4266) Edited by: Bittner M, Chen Y and Dorsel A. 2001, 132\u2013140."},{"key":"1465_CR14","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1021\/ac051495j","volume":"78","author":"S Bijlsma","year":"2006","unstructured":"Bijlsma S, Bobeldijk I, Verheij ER, Ramaker R, Kochhar S, Macdonald IA, vanOmmen B, Smilde AK: Large-scale human metabolomics studies: A strategy for data (pre-) processing and validation. Anal Chem 2006, 78: 567\u2013574. 10.1021\/ac051495j","journal-title":"Anal Chem"},{"key":"1465_CR15","volume-title":"The Statistical Analysis of Compositional Data","author":"J Aitchison","year":"2003","unstructured":"Aitchison J: The Statistical Analysis of Compositional Data. Caldwell, NJ, The Blackburn Press; 2003."},{"key":"1465_CR16","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1038\/372425a0","volume":"372","author":"Y Zhang","year":"1994","unstructured":"Zhang Y, Proenca R, Maffei M, Barone M, Leopold L, Friedman JM: Positional cloning of the mouse obese gene and its human homologue. Nature 1994, 372: 425\u2013432. 10.1038\/372425a0","journal-title":"Nature"},{"key":"1465_CR17","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1186\/1471-2105-6-179","volume":"6","author":"M Katajamaa","year":"2005","unstructured":"Katajamaa M, Oresic M: Processing methods for differential analysis of LC\/MS profile data. BMC Bioinformatics 2005, 6: 179. 10.1186\/1471-2105-6-179","journal-title":"BMC Bioinformatics"},{"key":"1465_CR18","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1093\/bioinformatics\/btg120","volume":"19","author":"R Steuer","year":"2003","unstructured":"Steuer R, Kurths J, Fiehn O, Weckwerth W: Observing and interpreting correlations in metabolomic networks. Bioinformatics 2003, 19: 1019\u20131026. 10.1093\/bioinformatics\/btg120","journal-title":"Bioinformatics"},{"key":"1465_CR19","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198524847.001.0001","volume-title":"Analysis of longitudinal data","author":"P Diggle","year":"2002","unstructured":"Diggle P, Heagerty P, Liang KY, Zeger S: Analysis of longitudinal data. 2nd edition edition. New York, Oxford University Press; 2002.","edition":"2nd edition"},{"key":"1465_CR20","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1093\/bioinformatics\/btk039","volume":"22","author":"M Katajamaa","year":"2006","unstructured":"Katajamaa M, Miettinen J, Oresic M: MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 2006, 22: 634\u2013636. 10.1093\/bioinformatics\/btk039","journal-title":"Bioinformatics"},{"key":"1465_CR21","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1021\/ac00073a010","volume":"66","author":"OM Kvalheim","year":"1994","unstructured":"Kvalheim OM, Brakstad F, Liang Y: Preprocessing of analytical profiles in the presence of homoscedastic or heteroscedastic noise. Anal Chem 1994, 66: 43\u201351. 10.1021\/ac00073a010","journal-title":"Anal Chem"},{"key":"1465_CR22","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1080\/01621459.1979.10481038","volume":"74","author":"WS Cleveland","year":"1979","unstructured":"Cleveland WS: Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 1979, 74: 829\u2013836. 10.2307\/2286407","journal-title":"J Am Stat Assoc"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1471-2105-8-93.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T01:53:02Z","timestamp":1707875582000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/1471-2105-8-93"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2007,3,15]]},"references-count":22,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2007,12]]}},"alternative-id":["1465"],"URL":"https:\/\/doi.org\/10.1186\/1471-2105-8-93","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2007,3,15]]},"assertion":[{"value":"17 November 2006","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 March 2007","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 March 2007","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"93"}}