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Probabilistic Quotient Normalization As Robust Method To Account For Dilution Of Complex Biological Mixtures. Application In 1H NMR Metabonomics.

Frank Dieterle, Alfred Ross, Goetz Schlotterbeck, Hans Senn
Published 2006 · Chemistry, Medicine
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For the analysis of the spectra of complex biofluids, preprocessing methods play a crucial role in rendering the subsequent data analyses more robust and accurate. Normalization is a preprocessing method, which accounts for different dilutions of samples by scaling the spectra to the same virtual overall concentration. In the field of 1H NMR metabonomics integral normalization, which scales spectra to the same total integral, is the de facto standard. In this work, it is shown that integral normalization is a suboptimal method for normalizing spectra from metabonomic studies. Especially strong metabonomic changes, evident as massive amounts of single metabolites in samples, significantly hamper the integral normalization resulting in incorrectly scaled spectra. The probabilistic quotient normalization is introduced in this work. This method is based on the calculation of a most probable dilution factor by looking at the distribution of the quotients of the amplitudes of a test spectrum by those of a reference spectrum. Simulated spectra, spectra of urine samples from a metabonomic study with cyclosporin-A as the active compound, and spectra of more than 4000 samples of control animals demonstrate that the probabilistic quotient normalization is by far more robust and more accurate than the widespread integral normalization and vector length normalization.



This paper is referenced by
10.1088/1752-7163/aa6e06
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Theodore R Mellors (2017)
10.5772/INTECHOPEN.69315
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Mario Pérez-Sayáns (2017)
10.1158/1541-7786.MCR-16-0262
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Stefano Cacciatore (2017)
10.1007/s11695-019-04294-5
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Ivana Jarak (2019)
10.1007/978-1-4939-3191-0_8
(1)H NMR Metabolomic Footprinting Analysis for the In Vitro Screening of Potential Chemopreventive Agents.
Luca Casadei (2016)
10.1182/BLOOD-2016-05-711846
PDGFB, a new candidate plasma biomarker for venous thromboembolism: results from the VEREMA affinity proteomics study.
Maria Bruzelius (2016)
10.1139/apnm-2016-0717
The impact of moderate altitude on exercise metabolism in recreational sportsmen: a nuclear magnetic resonance metabolomic approach.
Florian M Messier (2017)
10.1021/acs.jproteome.7b00879
Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data
Joram M. Posma (2018)
Chemometric applications in Nuclear Magnetic Resonance metabonomics
Gary Kirwan (2010)
10.1016/j.foodchem.2017.04.089
Metabolic profiling of apples from different production systems before and after controlled atmosphere (CA) storage studied by 1H high resolution-magic angle spinning (HR-MAS) NMR.
Martina Vermathen (2017)
10.1177/0003702817746947
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Víctor Olmos (2018)
Développement de nouvelles méthodes analytiques dans l'agroalimentaire par RMN
Clément Heude (2015)
Normalization of large-scale mass spectrometry-based metabolic profiling experiments
Bedilu Alamirie Ejigu (2012)
10.1039/c9en00933g
CdS nanoparticles in soil induce metabolic reprogramming in broad bean (Vicia faba L.) roots and leaves
Liyan Tian (2020)
10.1177/0162243919866898
Styles of Valuation: Algorithms and Agency in High-throughput Bioscience
Francis Lee (2020)
10.1186/1471-2164-15-947
Omics technologies provide new insights into the molecular physiopathology of equine osteochondrosis
Clémence Desjardin (2013)
10.1007/s11306-014-0693-3
Distinguishing between the metabolome and xenobiotic exposome in environmental field samples analysed by direct-infusion mass spectrometry based metabolomics and lipidomics
Andrew D. Southam (2014)
10.1155/2015/801691
1H NMR Metabolic Profiling of Biofluids from Rats with Gastric Mucosal Lesion and Electroacupuncture Treatment
Jingjing Xu (2015)
10.3390/cancers12061644
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Jesús María Urman (2020)
10.1016/j.phytochem.2014.09.009
Comparative LC-MS-based metabolite profiling of the ancient tropical rainforest tree Symphonia globulifera.
Kévin Cottet (2014)
10.1021/acs.analchem.5b00867
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Chaevien S. Clendinen (2015)
10.1016/J.CHEMOLAB.2013.07.007
Coefficient of Variation, Signal-to-Noise Ratio, and Effects of Normalization in Validation of Biomarkers from NMR-based Metabonomics Studies.
Bo Wang (2013)
10.1007/s00216-012-6165-6
NMR metabolomics for assessment of exercise effects with mouse biofluids
Laurence Le Moyec (2012)
10.1007/s11306-011-0355-7
Metabolomic investigations of Ricinus communis for cultivar and provenance determination
Eloise J. Pigott (2011)
10.1016/j.aca.2018.04.055
Model selection for within-batch effect correction in UPLC-MS metabolomics using quality control - Support vector regression.
Ángel Sánchez-Illana (2018)
10.1371/journal.pone.0020862
Biological Responses to Perfluorododecanoic Acid Exposure in Rat Kidneys as Determined by Integrated Proteomic and Metabonomic Studies
Hongxia Zhang (2011)
10.1021/jf200664t
Metabolic influence of Botrytis cinerea infection in champagne base wine.
Young-Shick Hong (2011)
10.1021/pr101054m
(1)H NMR-based metabolomic profiling in mice infected with Mycobacterium tuberculosis.
Ji-hyun Shin (2011)
10.1039/c000091d
Advances in NMR-based biofluid analysis and metabolite profiling.
Shucha Zhang (2010)
UHI Research Database pdf download summary Proteomics and Metabolomics for AKI Diagnosis
D. Marx (2019)
10.1002/ana.25244
A probiotic modulates the microbiome and immunity in multiple sclerosis
Stephanie K Tankou (2018)
10.1002/mnfr.201800384
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