The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Probabilistic Quotient Normalization as Robust Method to Account for Dilution of Complex Biological Mixtures. Application in 1H NMR Metabonomics
Abstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Nicholson, J. K., Lindon, J. C., & Holmes, E. (1999) · Xenobiotica · doi:10.1080/004982599238047 · Cited by 3,895
Metabolomics—the link between genotypes and phenotypes
Fiehn, O. (2002) · Plant Molecular Biology · doi:10.1023/a:1013713905833 · Cited by 3,445
Metabolomics — the link between genotypes and phenotypes
Fiehn, O. (2002) · Functional Genomics · doi:10.1007/978-94-010-0448-0_11 · Cited by 3,167
Handbook of Chemometrics and Qualimetrics
Massart, D. L., Vandeginste, B. G. M., Buydens, L. M. C., Lewi, P. J., Smeyers-Verbeke, J., & Jong, S. D. (1998) · Cited by 1,085
Scaling and Normalization Effects in NMR Spectroscopic Metabonomic Data Sets
Craig, A., Cloarec, O., Holmes, E., Nicholson, J. K., & Lindon, J. C. (2006) · Analytical Chemistry · doi:10.1021/ac0519312 · Cited by 494
NMR‐based metabonomic approaches for evaluating physiological influences on biofluid composition
Bollard, M. E., Stanley, E. G., Lindon, J. C., Nicholson, J. K., & Holmes, E. (2005) · NMR in Biomedicine · doi:10.1002/nbm.935 · Cited by 473
Lindon, J. C., Nicholson, J. K., Holmes, E., Antti, H., Bollard, M. E., Keun, H., Beckonert, O., Ebbels, T. M., Reily, M. D., Robertson, D., et al. (2003) · Toxicology and Applied Pharmacology · doi:10.1016/s0041-008x(02)00079-0 · Cited by 385
Self-citationPeak alignment of NMR signals by means of a genetic algorithm
Forshed, J., Schuppe‐Koistinen, I., & Jacobsson, S. P. (2003) · Analytica Chimica Acta · doi:10.1016/s0003-2670(03)00570-1 · Cited by 210
Holmes, E., Foxall, P. J. D., Nicholson, J. K., Neild, G. H., Brown, S. M., Beddell, C. R., Sweatman, B. C., Rahr, E., Lindon, J. C., Spraul, M., et al. (1994) · Analytical Biochemistry · doi:10.1006/abio.1994.1339 · Cited by 208
Metabolic profiling: a rosetta stone for genomics?
Trethewey, R. N., Krotzky, A. J., & Willmitzert, L. (1999) · Current Opinion in Plant Biology · doi:10.1016/s1369-5266(99)80017-x · Cited by 161
Cited by
De Filippis, F., Pellegrini, N., Vannini, L., Jeffery, I. B., La Storia, A., Laghi, L., Serrazanetti, D. I., Di Cagno, R., Ferrocino, I., Lazzi, C., et al. (2016) · Gut · doi:10.1136/gutjnl-2015-309957 · Cited by 1,638
Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis
Xia, J., & Wishart, D. S. (2016) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.11 · Cited by 1,544
Human metabolic phenotype diversity and its association with diet and blood pressure
Holmes, E., Loo, R. L., Stamler, J., Bictash, M., Yap, I. K. S., Chan, Q., Ebbels, T., De Iorio, M., Brown, I. J., Veselkov, K. A., et al. (2008) · Nature · doi:10.1038/nature06882 · Cited by 1,054
Fat, fibre and cancer risk in African Americans and rural Africans
O’Keefe, S. J. D., Li, J. V., Lahti, L., Ou, J., Carbonero, F., Mohammed, K., Posma, J. M., Kinross, J., Wahl, E., Ruder, E., et al. (2015) · Nature Communications · doi:10.1038/ncomms7342 · Cited by 982
Molecular phenomics and metagenomics of hepatic steatosis in non-diabetic obese women
Hoyles, L., Fernández-Real, J.-M., Federici, M., Serino, M., Abbott, J., Charpentier, J., Heymes, C., Luque, J. L., Anthony, E., Barton, R. H., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0061-3 · Cited by 720
Reflections on univariate and multivariate analysis of metabolomics data
Saccenti, E., Hoefsloot, H. C. J., Smilde, A. K., Westerhuis, J. A., & Hendriks, M. M. W. B. (2014) · Metabolomics · doi:10.1007/s11306-013-0598-6 · Cited by 658
Magnetic Resonance Spectroscopy Identifies Neural Progenitor Cells in the Live Human Brain
Manganas, L. N., Zhang, X., Li, Y., Hazel, R. D., Smith, S. D., Wagshul, M. E., Henn, F., Benveniste, H., Djurić, P. M., Enikolopov, G., et al. (2007) · Science · doi:10.1126/science.1147851 · Cited by 484
Chan, E. C. Y., Koh, P. K., Mal, M., Cheah, P. Y., Eu, K. W., Backshall, A., Cavill, R., Nicholson, J. K., & Keun, H. C. (2009) · Journal of Proteome Research · doi:10.1021/pr8006232 · Cited by 444
High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues
Beckonert, O., Coen, M., Keun, H. C., Wang, Y., Ebbels, T. M. D., Holmes, E., Lindon, J. C., & Nicholson, J. K. (2010) · Nature Protocols · doi:10.1038/nprot.2010.45 · Cited by 381
Di Guida, R., Engel, J., Allwood, J. W., Weber, R. J. M., Jones, M. R., Sommer, U., Viant, M. R., & Dunn, W. B. (2016) · Metabolomics · doi:10.1007/s11306-016-1030-9 · Cited by 371
High‐Throughput Metabolomics by 1D NMR
Vignoli, A., Ghini, V., Meoni, G., Licari, C., Takis, P. G., Tenori, L., Turano, P., & Luchinat, C. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201804736 · Cited by 365
Gorrochategui, E., Jaumot, J., Lacorte, S., & Tauler, R. (2016) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2016.07.004 · Cited by 352
NMR-based metabolomics in human disease diagnosis: applications, limitations, and recommendations
Emwas, A.-H. M., Salek, R. M., Griffin, J. L., & Merzaban, J. (2013) · Metabolomics · doi:10.1007/s11306-013-0524-y · Cited by 296
Heinzmann, S. S., Brown, I. J., Chan, Q., Bictash, M., Dumas, M.-E., Kochhar, S., Stamler, J., Holmes, E., Elliott, P., & Nicholson, J. K. (2010) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.2010.29672 · Cited by 271
Human Early Life Exposome (HELIX) study: a European population-based exposome cohort
Maitre, L., de Bont, J., Casas, M., Robinson, O., Aasvang, G. M., Agier, L., Andrušaitytė, S., Ballester, F., Basagaña, X., Borràs, E., et al. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-021311 · Cited by 267
A probiotic modulates the microbiome and immunity in multiple sclerosis
Tankou, S. K., Regev, K., Healy, B. C., Tjon, E., Laghi, L., Cox, L. M., Kivisäkk, P., Pierre, I. V., Hrishikesh, L., Gandhi, R., et al. (2018) · Annals of Neurology · doi:10.1002/ana.25244 · Cited by 264
Cunningham, J. T., Moreno, M. V., Lodi, A., Ronen, S. M., & Ruggero, D. (2014) · Cell · doi:10.1016/j.cell.2014.03.052 · Cited by 260
1H NMR-based Metabolomic Profiling in Mice Infected with Mycobacterium tuberculosis
Shin, J.-H., Yang, J.-Y., Jeon, B.-Y., Yoon, Y. J., Cho, S.-N., Kang, Y.-H., Ryu, D. H., & Hwang, G.-S. (2011) · Journal of Proteome Research · doi:10.1021/pr101054m · Cited by 247
The impact of skin care products on skin chemistry and microbiome dynamics
Bouslimani, A., da Silva, R., Kosciolek, T., Janssen, S., Callewaert, C., Amir, A., Dorrestein, K., Melnik, A. V., Zaramela, L. S., Kim, J.-N., et al. (2019) · BMC Biology · doi:10.1186/s12915-019-0660-6 · Cited by 234
Analytical and statistical approaches to metabolomics research
Issaq, H. J., Van, Q. N., Waybright, T. J., Muschik, G. M., & Veenstra, T. D. (2009) · Journal of Separation Science · doi:10.1002/jssc.200900152 · Cited by 227
Measuring Biological Age via Metabonomics: The Metabolic Age Score
Hertel, J., Friedrich, N., Wittfeld, K., Pietzner, M., Budde, K., Van der Auwera, S., Lohmann, T., Teumer, A., Völzke, H., Nauck, M., et al. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00561 · Cited by 181
Multilevel data analysis of a crossover designed human nutritional intervention study
van Velzen, E. J. J., Westerhuis, J. A., van Duynhoven, J. P. M., van Dorsten, F. A., Hoefsloot, H. C. J., Jacobs, D. M., Smit, S., Draijer, R., Kroner, C. I., & Smilde, A. K. (2008) · Journal of Proteome Research · doi:10.1021/pr800145j · Cited by 161
De Filippis, F., Vannini, L., La Storia, A., Laghi, L., Piombino, P., Stellato, G., Serrazanetti, D. I., Gozzi, G., Turroni, S., Ferrocino, I., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112373 · Cited by 146
Diurnal rhythms in the human urine metabolome during sleep and total sleep deprivation
Giskeødegård, G. F., Davies, S. K., Revell, V. L., Keun, H., & Skene, D. J. (2015) · Scientific Reports · doi:10.1038/srep14843 · Cited by 135
Edmands, W. M. B., Beckonert, O. P., Stella, C., Campbell, A., Lake, B. G., Lindon, J. C., Holmes, E., & Gooderham, N. J. (2011) · Journal of Proteome Research · doi:10.1021/pr200326k · Cited by 119
Dumas, M.-E., Rothwell, A. R., Hoyles, L., Aranias, T., Chilloux, J., Calderari, S., Noll, E. M., Péan, N., Boulangé, C. L., Blancher, C., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.06.039 · Cited by 114
“TMAO results from a phase 1 N-oxidation of gut microbial TMA (al-Waiz et al., 1992) catabolized by FMO3 in humans (Dolphin et al., 1997).”
BackgroundGut microbiota modulate the metabolism of brown adipose tissue in mice
Mestdagh, R., Dumas, M.-E., Rezzi, S., Kochhar, S., Holmes, E., Claus, S. P., & Nicholson, J. K. (2012) · Journal of Proteome Research · doi:10.1021/pr200938v · Cited by 97
Fajstova, A., Galanova, N., Coufal, S., Malkova, J., Kostovcik, M., Cermakova, M., Pelantova, H., Kuzma, M., Sediva, B., Hudcovic, T., et al. (2020) · Cells · doi:10.3390/cells9122701 · Cited by 90
Bacterially produced metabolites protect C. elegans neurons from degeneration
Urrutia, A., García-Angulo, V. A., Fuentes, A., Caneo, M., Legüe, M., Urquiza, S., Delgado, S. E., Ugalde, J., Burdisso, P., & Calixto, A. (2020) · PLOS Biology · doi:10.1371/journal.pbio.3000638 · Cited by 71
Large neutral amino acid levels tune perinatal neuronal excitability and survival
Knaus, L. S., Basilico, B., Malzl, D., Gerykova Bujalkova, M., Smogavec, M., Schwarz, L. A., Gorkiewicz, S., Amberg, N., Pauler, F. M., Knittl-Frank, C., et al. (2023) · Cell · doi:10.1016/j.cell.2023.02.037 · Cited by 70
Tiziani, S., Kang, Y., Choi, J. S., Roberts, W., & Paternostro, G. (2011) · Nature Communications · doi:10.1038/ncomms1562 · Cited by 60
Effect of Breadmaking Process on In Vitro Gut Microbiota Parameters in Irritable Bowel Syndrome
Costabile, A., Santarelli, S., Claus, S. P., Sanderson, J., Hudspith, B. N., Brostoff, J., Ward, J. L., Lovegrove, A., Shewry, P. R., Jones, H. E., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0111225 · Cited by 52
Malecki, M., Kamrad, S., Ralser, M., & Bähler, J. (2020) · The EMBO Reports · doi:10.15252/embr.202050845 · Cited by 50
Anti-Cancer Effects of Emodin on HepG2 Cells as Revealed by 1H NMR Based Metabolic Profiling
Xing, Y.-X., Li, M.-H., Tao, L., Ruan, L.-Y., Hong, W., Chen, C., Zhao, W.-L., Xu, H., Chen, J.-F., & Wang, J.-S. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00029 · Cited by 46
Backshall, A., Alferez, D., Teichert, F., Wilson, I. D., Wilkinson, R. W., Goodlad, R. A., & Keun, H. C. (2009) · Journal of Proteome Research · doi:10.1021/pr800793w · Cited by 42
Cheng, Y., Schlosser, P., Hertel, J., Sekula, P., Oefner, P. J., Spiekerkoetter, U., Mielke, J., Freitag, D. F., Schmidts, M., GCKD Investigators, et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20877-8 · Cited by 37
Martinez-Gili, L., Pechlivanis, A., McDonald, J. A. K., Begum, S., Badrock, J., Dyson, J. K., Jones, R., Hirschfield, G., Ryder, S. D., Sandford, R., et al. (2023) · Gut Microbes · doi:10.1080/19490976.2023.2208501 · Cited by 32
Sniffing out the hypoxia volatile metabolic signature of Aspergillus fumigatus
Rees, C. A., Stefanuto, P.-H., Beattie, S. R., Bultman, K. M., Cramer, R. A., & Hill, J. E. (2017) · Journal of Breath Research · doi:10.1088/1752-7163/aa7b3e · Cited by 29
Robinson, O., Lau, C. E., Joo, S., Andrusaityte, S., Borras, E., de Prado-Bert, P., Chatzi, L., Keun, H. C., Grazuleviciene, R., Gutzkow, K. B., et al. (2023) · eLife · doi:10.7554/elife.85104 · Cited by 20
Detection of early prognostic biomarkers for metastasis of Ewing's sarcoma in pediatric patients
Ezzeldin, S., Osama, A., Anwar, A. M., Mahgoub, S., Ahmed, E. A., Farid, N., Zamzam, M., El Ghoneimy, A., & Magdeldin, S. (2023) · Life Sciences · doi:10.1016/j.lfs.2023.122237 · Cited by 12
Metadata
- Authors
- Dieterle, F., Ross, A., Schlotterbeck, G., & Senn, H.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2006-05-28
- Volume
- 78
- Issue
- 13
- Pages
- 4281-4290
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2083637865
- Cited by
- 1,768
- References
- 14
- FWCI
- 5.40
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
