The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
A Strategy for Identifying Differences in Large Series of Metabolomic Samples Analyzed by GC/MS
Abstract
In metabolomics, the purpose is to identify and quantify all the metabolites in a biological system. Combined gas chromatography and mass spectrometry (GC/MS) is one of the most commonly used techniques in metabolomics together with 1H NMR, and it has been shown that more than 300 compounds can be distinguished with GC/MS after deconvolution of overlapping peaks. To avoid having to deconvolute all analyzed samples prior to multivariate analysis of the data, we have developed a strategy for rapid comparison of nonprocessed MS data files. The method includes baseline correction, alignment, time window determinations, alternating regression, PLS-DA, and identification of retention time windows in the chromatograms that explain the differences between the samples. Use of alternating regression also gives interpretable loadings, which retain the information provided by m/z values that vary between the samples in each retention time window. The method has been applied to plant extracts derived from leaves of different developmental stages and plants subjected to small changes in day length. The data show that the new method can detect differences between the samples and that it gives results comparable to those obtained when deconvolution is applied prior to the multivariate analysis. We suggest that this method can be used for rapid comparison of large sets of GC/MS data, thereby applying time-consuming deconvolution only to parts of the chromatograms that contribute to explain the differences between the samples.
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
Wold, S., Esbensen, K., & Geladi, P. (1987) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(87)80084-9 · Cited by 11,606
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
Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models
Wold, S. (1978) · Technometrics · doi:10.1080/00401706.1978.10489693 · Cited by 2,601
Metabolite profiling for plant functional genomics
Fiehn, O., Kopka, J., Dörmann, P., Altmann, T., Trethewey, R. N., & Willmitzer, L. (2000) · Nature Biotechnology · doi:10.1038/81137 · Cited by 2,127
Metabonomics: a platform for studying drug toxicity and gene function
Nicholson, J. K., Connelly, J., Lindon, J. C., & Holmes, E. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd728 · Cited by 1,914
Plant metabolomics: large-scale phytochemistry in the functional genomics era
Sumner, L. W., Mendes, P., & Dixon, R. A. (2003) · Phytochemistry · doi:10.1016/s0031-9422(02)00708-2 · Cited by 1,093
High-throughput classification of yeast mutants for functional genomics using metabolic footprinting
Allen, J., Davey, H. M., Broadhurst, D., Heald, J. K., Rowland, J. J., Oliver, S. G., & Kell, D. B. (2003) · Nature Biotechnology · doi:10.1038/nbt823 · Cited by 538
Kvalheim, O. M., & Liang, Y. Z. (1992) · Analytical Chemistry · doi:10.1021/ac00032a019 · Cited by 451
Liang, Y. Z., Kvalheim, O. M., Keller, H. R., Massart, D. L., Kiechle, P., & Erni, F. (1992) · Analytical Chemistry · doi:10.1021/ac00032a020 · Cited by 324
Cited by
Gut microorganisms, mammalian metabolism and personalized health care
Nicholson, J. K., Holmes, E., & Wilson, I. D. (2005) · Nature Reviews Microbiology · doi:10.1038/nrmicro1152 · Cited by 945
Metabolomics for Functional Genomics, Systems Biology, and Biotechnology
Saito, K., & Matsuda, F. (2010) · Annual Review of Plant Biology · doi:10.1146/annurev.arplant.043008.092035 · Cited by 744
Characterization of the ABA‐regulated global responses to dehydration in Arabidopsis by metabolomics
Urano, K., Maruyama, K., Ogata, Y., Morishita, Y., Takeda, M., Sakurai, N., Suzuki, H., Saito, K., Shibata, D., Kobayashi, M., et al. (2009) · The Plant Journal · doi:10.1111/j.1365-313x.2008.03748.x · Cited by 591
Metabolomics and systems biology: making sense of the soup
KELL, D. (2004) · Current Opinion in Microbiology · doi:10.1016/j.mib.2004.04.012 · Cited by 529
Tikunov, Y., Lommen, A., de Vos, C. H. R., Verhoeven, H. A., Bino, R. J., Hall, R. D., & Bovy, A. G. (2005) · Plant Physiology · doi:10.1104/pp.105.068130 · Cited by 513
Plant metabolomics: from holistic hope, to hype, to hot topic
Hall, R. D. (2006) · New Phytologist · doi:10.1111/j.1469-8137.2005.01632.x · Cited by 506
Gullberg, J., Jonsson, P., Nordström, A., Sjöström, M., & Moritz, T. (2004) · Analytical Biochemistry · doi:10.1016/j.ab.2004.04.037 · Cited by 503
Self-citationHPLC-MS-based methods for the study of metabonomics
WILSON, I., PLUMB, R., GRANGER, J., MAJOR, H., WILLIAMS, R., & LENZ, E. (2005) · Journal of Chromatography B · doi:10.1016/j.jchromb.2004.07.045 · Cited by 447
Metabolomics technology and bioinformatics
Shulaev, V. (2006) · Briefings in Bioinformatics · doi:10.1093/bib/bbl012 · Cited by 408
Serum Metabolite Profiling of Human Colorectal Cancer Using GC−TOFMS and UPLC−QTOFMS
Qiu, Y., Cai, G., Su, M., Chen, T., Zheng, X., Xu, Y., Ni, Y., Zhao, A., Xu, L. X., Cai, S., et al. (2009) · Journal of Proteome Research · doi:10.1021/pr9004162 · Cited by 395
Serum and Urine Metabolite Profiling Reveals Potential Biomarkers of Human Hepatocellular Carcinoma
Chen, T., Xie, G., Wang, X., Fan, J., Qiu, Y., Zheng, X., Qi, X., Cao, Y., Su, M., Wang, X., et al. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m110.004945 · Cited by 357
Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives
Koek, M. M., Jellema, R. H., van der Greef, J., Tas, A. C., & Hankemeier, T. (2011) · Metabolomics · doi:10.1007/s11306-010-0254-3 · Cited by 353
Crockford, D. J., Holmes, E., Lindon, J. C., Plumb, R. S., Zirah, S., Bruce, S. J., Rainville, P., Stumpf, C. L., & Nicholson, J. K. (2006) · Analytical Chemistry · doi:10.1021/ac051444m · Cited by 342
Integration of GC–MS and LC–MS for untargeted metabolomics profiling
Zeki, Ö. C., Eylem, C. C., Reçber, T., Kır, S., & Nemutlu, E. (2020) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/j.jpba.2020.113509 · Cited by 336
Metabolomic technologies and their application to the study of plants and plant–host interactions
Allwood, J. W., Ellis, D. I., & Goodacre, R. (2008) · Physiologia Plantarum · doi:10.1111/j.1399-3054.2007.01001.x · Cited by 301
Dunn, W. B. (2008) · Physical Biology · doi:10.1088/1478-3975/5/1/011001 · Cited by 278
Nordström, A., O'Maille, G., Qin, C., & Siuzdak, G. (2006) · Analytical Chemistry · doi:10.1021/ac060245f · Cited by 273
Self-citationPotential metabolite markers of schizophrenia
Yang, J., Chen, T., Sun, L., Zhao, Z., Qi, X., Zhou, K., Cao, Y., Wang, X., Qiu, Y., Su, M., et al. (2013) · Molecular Psychiatry · doi:10.1038/mp.2011.131 · Cited by 253
Evaluating and minimizing batch effects in metabolomics
Han, W., & Li, L. (2022) · Mass Spectrometry Reviews · doi:10.1002/mas.21672 · Cited by 105
Metadata
- Authors
- Jonsson, P., Gullberg, J., Nordström, A., Kusano, M., Kowalczyk, M., Sjöström, M., & Moritz, T.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2004-02-11
- Volume
- 76
- Issue
- 6
- Pages
- 1738-1745
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2061445382
- Cited by
- 349
- References
- 17
- FWCI
- 9.62
- 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.
