SpringerLink
Log in

Normalization methods for reducing interbatch effect without quality control samples in liquid chromatography-mass spectrometry-based studies

  • Research Paper
  • Published:
Analytical and Bioanalytical Chemistry Aims and scope Submit manuscript

Abstract

Data normalization is an essential part of a large-scale untargeted mass spectrometry metabolomics analysis. Autoscaling, Pareto scaling, range scaling, and level scaling methods for liquid chromatography-mass spectrometry data processing were compared with the most common normalization methods, including quantile normalization, probabilistic quotient normalization, and variance stabilizing normalization. These methods were tested on eight datasets from various clinical studies. The efficiency of the data normalization was assessed by the distance between clusters corresponding to batches and the distance between clusters corresponding to clinical groups in the space of principal components, as well as by the number of features with a pairwise statistically significant difference between the batches and the number of features with a pairwise statistically significant difference between clinical groups. Autoscaling demonstrated the most effective reduction in interbatch variation and can be preferable to probabilistic quotient or quantile normalization in liquid chromatography-mass spectrometry data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Thailand)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Bijlsma S, Bobeldijk I, Verheij ER, Ramaker R, Kochhar S, Macdonald IA, et al. Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation. Anal Chem. 2006;78:567–74. https://doi.org/10.1021/ac051495j.

    Article  CAS  PubMed  Google Scholar 

  2. Koelmel JP, Cochran JA, Ulmer CZ, Levy AJ, Patterson RE, Olsen BC, et al. Software tool for internal standard based normalization of lipids, and effect of data-processing strategies on resulting values. BMC Bioinformatics. 2019;20:1–13. https://doi.org/10.1186/s12859-019-2803-8.

    Article  Google Scholar 

  3. Sysi-Aho M, Katajamaa M, Yetukuri L, Orešič M. Normalization method for metabolomics data using optimal selection of multiple internal standards. BMC Bioinformatics. 2007;8:1–17. https://doi.org/10.1186/1471-2105-8-93.

    Article  CAS  Google Scholar 

  4. De Livera AM, Sysi-Aho M, Jacob L, Gagnon-Bartsch JA, Castillo S, Simpson JA, et al. Statistical methods for handling unwanted variation in metabolomics data. Anal Chem. 2015;87:3606–15. https://doi.org/10.1021/ac502439y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Wang SY, Kuo CH, Tseng YJ. Batch normalizer: a fast total abundance regression calibration method to simultaneously adjust batch and injection order effects in liquid chromatography/time-of-flight mass spectrometry-based metabolomics data and comparison with current calibration met. Anal Chem. 2013;85:1037–46. https://doi.org/10.1021/ac302877x.

    Article  CAS  PubMed  Google Scholar 

  6. Shen X, Gong X, Cai Y, Guo Y, Tu J, Li H, et al. Normalization and integration of large-scale metabolomics data using support vector regression. Metabolomics. 2016;12:1–12. https://doi.org/10.1007/s11306-016-1026-5.

    Article  CAS  Google Scholar 

  7. Dieterle F, Ross A, Schlotterbeck G, Senn H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in1H NMR metabonomics. Anal Chem. 2006;78:4281–90. https://doi.org/10.1021/ac051632c.

    Article  CAS  PubMed  Google Scholar 

  8. Han RH, Wang M, Fang X, Han X. Simulation of triacylglycerol ion profiles: bioinformatics for interpretation of triacylglycerol biosynthesis. J Lipid Res. 2013;54:1023–32. https://doi.org/10.1194/jlr.M033837.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. van den Berg RA, Hoefsloot HCJ, Westerhuis JA, Smilde AK, van der Werf MJ. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics. 2006;7:1–15. https://doi.org/10.1186/1471-2164-7-142.

    Article  CAS  Google Scholar 

  10. Karaman I. Preprocessing and pretreatment of metabolomics data for statistical. Analysis. 2017;965:145–61. https://doi.org/10.1007/978-3-319-47656-8.

    Article  CAS  Google Scholar 

  11. Du YM, Hu Y, **a Y, Ouyang Z. Power normalization for mass spectrometry data analysis and analytical method assessment. Anal Chem. 2016;88:3156–63. https://doi.org/10.1021/acs.analchem.5b04418.

    Article  CAS  PubMed  Google Scholar 

  12. Huber W, Von Heydebreck A, Sültmann H, Poustka A, Vingron M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics. 2002;18. https://doi.org/10.1093/bioinformatics/18.suppl_1.S96.

  13. Lin SM, Du P, Huber W, Kibbe WA. Model-based variance-stabilizing transformation for Illumina microarray data. Nucleic Acids Res. 2008;36:1–9. https://doi.org/10.1093/nar/gkm1075.

    Article  CAS  Google Scholar 

  14. Chawade A, Alexandersson E, Levander F. Normalyzer: a tool for rapid evaluation of normalization methods for omics data sets. J Proteome Res. 2014;13:3114–20. https://doi.org/10.1021/pr401264n.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Li B, Tang J, Yang Q, Cui X, Li S, Chen S, et al. Performance evaluation and online realization of data-driven normalization methods used in LC/MS based untargeted metabolomics analysis. Sci Rep. 2016;6:1–13. https://doi.org/10.1038/srep38881.

    Article  CAS  Google Scholar 

  16. Lee J, Park J, Lim MS, Seong SJ, Seo JJ, Park SM, et al. Quantile normalization approach for liquid chromatography- mass spectrometry-based metabolomic data from healthy human volunteers. Anal Sci. 2012;28:801–5. https://doi.org/10.2116/analsci.28.801.

    Article  CAS  PubMed  Google Scholar 

  17. Yang Q, Wang Y, Zhang Y, Li F, **a W, Zhou Y, et al. NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data. Nucleic Acids Res. 2020;48:W436–48. https://doi.org/10.1093/nar/gkaa258.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Chen J, Zhang P, Lv M, Guo H, Huang Y, Zhang Z, et al. Influences of normalization method on biomarker discovery in gas chromatography-mass spectrometry-based untargeted metabolomics: what should be considered? Anal Chem. 2017;89:5342–8. https://doi.org/10.1021/acs.analchem.6b05152.

    Article  CAS  PubMed  Google Scholar 

  19. Ejigu BA, Valkenborg D, Baggerman G, Vanaerschot M, Witters E, Dujardin JC, et al. Evaluation of normalization methods to pave the way towards large-scale LC-MS-based metabolomics profiling experiments. Omi A J Integr Biol. 2013;17:473–85. https://doi.org/10.1089/omi.2013.0010.

    Article  CAS  Google Scholar 

  20. Ly-Verdú S, Gröger TM, Arteaga-Salas JM, Brandmaier S, Kahle M, Neschen S, et al. Combining metabolomic non-targeted GC×GC-ToF-MS analysis and chemometric ASCA-based study of variances to assess dietary influence on type 2 diabetes development in a mouse model. Anal Bioanal Chem. 2015;407:343–54. https://doi.org/10.1007/s00216-014-8227-4.

    Article  CAS  PubMed  Google Scholar 

  21. Li B, Tang J, Yang Q, Li S, Cui X, Li Y, et al. NOREVA: normalization and evaluation of MS-based metabolomics data. Nucleic Acids Res. 2017;45:W162–70. https://doi.org/10.1093/nar/gkx449.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Chagovets V, Wang Z, Kononikhin A, Starodubtseva N, Borisova A, Salimova D, et al. A comparison of tissue spray and lipid extract direct injection electrospray ionization mass spectrometry for the differentiation of Eutopic and ectopic endometrial tissues. J Am Soc Mass Spectrom. 2018;29:323–30. https://doi.org/10.1007/s13361-017-1792-y.

    Article  CAS  PubMed  Google Scholar 

  23. Tokareva AO, Chagovets VV, Rodionov VV, Kometova VV, Rodionova MV, Starodubtseva NL, Frankevich VE. Lipid markers of metastatic lesions in regional lymph nodes in patients with breast cancer. Akusherstvo i Ginekol (Russian Fed) 2020:133–40.

  24. Starodubtseva N, Chagovets V, Borisova A, Salimova D, Aleksandrova N, Chingin K, et al. Identification of potential endometriosis biomarkers in peritoneal fluid and blood plasma via shotgun lipidomics. Clin Mass Spectrom. 2019;13:21–6. https://doi.org/10.1016/j.clinms.2019.05.007.

    Article  Google Scholar 

  25. Kan NE, Khachatryan ZV, Chagovets VV, Starodubtseva NL, Amiraslanov EY, Tyutyunnik VL, et al. Analysis of metabolic pathways in intrauterine growth restriction. Biomeditsinskaya Khimiya. 2020;66:174–80. https://doi.org/10.18097/PBMC20206602174.

  26. Tonoyan NM, Tokareva AO, Chagovets V V., Starodubtseva NL, Kozachenko IF, Adamyan LV. Possibilities for predicting recurrent uterine myoma by plasma lipidomic analysis. Akusherstvo i Ginekol (Russian Fed 2019:136–151.

  27. Cook T, Ma Y, Gamagedara S. Evaluation of statistical techniques to normalize mass spectrometry-based urinary metabolomics data. J Pharm Biomed Anal. 2020;177:112854. https://doi.org/10.1016/j.jpba.2019.112854.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work was financially supported by the Russian Science Foundation (No. 18-75-10097). The authors are grateful to the laboratory for the collection and storage of biological material (biobank) for providing tissue samples.

Author information

Authors and Affiliations

  1. National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov of the Ministry of Healthcare of the Russian Federation, Moscow, 117997, Russia

    Alisa O. Tokareva, Vitaliy V. Chagovets, Alexey S. Kononikhin, Natalia L. Starodubtseva & Vladimir E. Frankevich

  2. V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Center of Chemical Physic, Russian Academy of Sciences, Moscow, 119334, Russia

    Alisa O. Tokareva

  3. Moscow Institute of Physics and Technology, Moscow, 141701, Russia

    Alisa O. Tokareva & Natalia L. Starodubtseva

  4. Skolkovo Institute of Science and Technology, Moscow, 121205, Russia

    Alexey S. Kononikhin & Eugene N. Nikolaev

Authors
  1. Alisa O. Tokareva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vitaliy V. Chagovets
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexey S. Kononikhin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Natalia L. Starodubtseva
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eugene N. Nikolaev
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Vladimir E. Frankevich
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Natalia L. Starodubtseva or Vladimir E. Frankevich.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

ESM 1

(PDF 712 kb)

ESM 2

(XLSX 43526 kb)

ESM 3

(XLSX 1283 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tokareva, A.O., Chagovets, V.V., Kononikhin, A.S. et al. Normalization methods for reducing interbatch effect without quality control samples in liquid chromatography-mass spectrometry-based studies. Anal Bioanal Chem 413, 3479–3486 (2021). https://doi.org/10.1007/s00216-021-03294-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-021-03294-8

Keywords

Search

Navigation