Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Statistical analysis in metabolic phenotyping

Nature Protocols volume 16, pages 4299–4326 (2021)Cite this article

Subjects

Abstract

Metabolic phenotyping is an important tool in translational biomedical research. The advanced analytical technologies commonly used for phenotyping, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, generate complex data requiring tailored statistical analysis methods. Detailed protocols have been published for data acquisition by liquid NMR, solid-state NMR, ultra-performance liquid chromatography (LC-)MS and gas chromatography (GC-)MS on biofluids or tissues and their preprocessing. Here we propose an efficient protocol (guidelines and software) for statistical analysis of metabolic data generated by these methods. Code for all steps is provided, and no prior coding skill is necessary. We offer efficient solutions for the different steps required within the complete phenotyping data analytics workflow: scaling, normalization, outlier detection, multivariate analysis to explore and model study-related effects, selection of candidate biomarkers, validation, multiple testing correction and performance evaluation of statistical models. We also provide a statistical power calculation algorithm and safeguards to ensure robust and meaningful experimental designs that deliver reliable results. We exemplify the protocol with a two-group classification study and data from an epidemiological cohort; however, the protocol can be easily modified to cover a wider range of experimental designs or incorporate different modeling approaches. This protocol describes a minimal set of analyses needed to rigorously investigate typical datasets encountered in metabolic phenotyping.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Typical metabolic phenotyping workflow.
Fig. 2: Metabolic and outcome data.
Fig. 3: PCA and outlier detection.
Fig. 4: Partial least squares and orthogonal partial least squares regression.
Fig. 5: Cross-validation of multivariate models.
Fig. 6: Determination of model validity using random permutation.
Fig. 7: Unsupervised analysis.
Fig. 8: Supervised analysis.

Similar content being viewed by others

Data availability

All the data and software reported in the paper are open source and freely available on GitHub and Zenodo repositories: https://github.com/Gscorreia89/chemometrics-tutorials, https://github.com/phenomecentre/metabotyping-dementia-urine and https://doi.org/10.5281/zenodo.4053166.

Code availability

All the data and software reported in the paper are open source and freely available on GitHub and Zenodo repositories: https://github.com/Gscorreia89/chemometrics-tutorials, https://github.com/phenomecentre/metabotyping-dementia-urine and https://doi.org/10.5281/zenodo.4053166.

References

  1. Nicholson, J. K., Lindon, J. C. & Holmes, E. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29, 1181–1189 (2008).

    Article  Google Scholar 

  2. Holmes, E., Wilson, I. D. & Nicholson, J. K. Metabolic phenotyping in health and disease. Cell 134, 714–717 (2008).

    Article  CAS  PubMed  Google Scholar 

  3. Nicholson, J. K. et al. Metabolic phenotyping in clinical and surgical environments. Nature 491, 384–392 (2012).

    Article  CAS  PubMed  Google Scholar 

  4. Surowiec, I. et al. Quantification of run order effect on chromatography - mass spectrometry profiling data. J. Chromatogr. A 1568, 229–234 (2018).

    Article  CAS  PubMed  Google Scholar 

  5. Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010).

    Article  CAS  PubMed  Google Scholar 

  6. Lewis, M. R. et al. Development and application of UPLC-ToF MS for precision large scale urinary metabolic phenotyping. Anal. Chem. https://doi.org/10.1021/acs.analchem.6b01481 (2016).

  7. Fages, A. et al. Batch profiling calibration for robust NMR metabonomic data analysis. Anal. Bioanal. Chem. 405, 8819–8827 (2013).

    Article  CAS  PubMed  Google Scholar 

  8. Dieterle, F., Ross, A., Schlotterbeck, G. & Senn, H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal. Chem. 78, 4281–4290 (2006).

    Article  CAS  PubMed  Google Scholar 

  9. Posma, J. M. et al. Optimized phenotypic biomarker discovery and confounder elimination via covariate-adjusted projection to latent structures from metabolic spectroscopy data. J. Proteome Res. https://doi.org/10.1021/acs.jproteome.7b00879 (2018).

  10. Blaise, B. J. et al. Statistical recoupling prior to significance testing in nuclear magnetic resonance based metabonomics. Anal. Chem. 81, 6242–6251 (2009).

    Article  CAS  PubMed  Google Scholar 

  11. Navratil, V., Pontoizeau, C., Billoir, E. & Blaise, B. J. SRV: an open-source toolbox to accelerate the recovery of metabolic biomarkers and correlations from metabolic phenotyping datasets. Bioinformatics 29, 1348–1349 (2013).

    Article  CAS  PubMed  Google Scholar 

  12. Kuhl, C., Tautenhahn, R., Böttcher, C., Larson, T. R. & Neumann, S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal. Chem. 84, 283–289 (2012).

    Article  CAS  PubMed  Google Scholar 

  13. Moseley, H. N. B. Error analysis and propagation in metabolomics data analysis. Comput. Struct. Biotechnol. J. 4, e201301006 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).

    Article  CAS  PubMed  Google Scholar 

  15. Forsberg, E. M. et al. Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online. Nat. Protoc. https://doi.org/10.1038/nprot.2017.151 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Pluskal, T. T., Castillo, S., Villar-Briones, A., Oresic, M. & Orešič, M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinforma. 11, 395 (2010).

    Article  CAS  Google Scholar 

  17. Li, B. et al. NOREVA: normalization and evaluation of MS-based metabolomics data. Nucleic Acids Res. https://doi.org/10.1093/nar/gkx449 (2017).

  18. Hughes, G. et al. MSPrep—summarization, normalization and diagnostics for processing of mass spectrometry-based metabolomic data. Bioinformatics https://doi.org/10.1093/bioinformatics/btt589 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Wehrens, R., Weingart, G. & Mattivi, F. MetaMS: an open-source pipeline for GC-MS-based untargeted metabolomics. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. https://doi.org/10.1016/j.jchromb.2014.02.051 (2014).

    Article  Google Scholar 

  20. Wang, S. & Yang, H. pseudoQC: a regression-based simulation software for correction and normalization of complex metabolomics and proteomics datasets. Proteomics https://doi.org/10.1002/pmic.201900264 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Biswas, A. et al. Metdat: a modular and workflow-based free online pipeline for mass spectrometry data processing, analysis and interpretation. Bioinformatics https://doi.org/10.1093/bioinformatics/btq436 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Shen, X., Zhu, Z. J. & Wren, J. MetFlow: an interactive and integrated workflow for metabolomics data cleaning and differential metabolite discovery. Bioinformatics https://doi.org/10.1093/bioinformatics/bty1066 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Hao, L. et al. Metandem: an online software tool for mass spectrometry-based isobaric labeling metabolomics. Anal. Chim. Acta https://doi.org/10.1016/j.aca.2019.08.046 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Verhoeven, A., Giera, M. & Mayboroda, O. A. KIMBLE: a versatile visual NMR metabolomics workbench in KNIME. Anal. Chim. Acta https://doi.org/10.1016/j.aca.2018.07.070 (2018).

    Article  PubMed  Google Scholar 

  25. Hao, J., Astle, W., De Iorio, M. & Ebbels, T. M. D. BATMAN—an R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model. Bioinformatics 28, 2088–2090 (2012).

    Article  CAS  PubMed  Google Scholar 

  26. Beirnaert, C. et al. speaq 2.0: A complete workflow for high-throughput 1D NMR spectra processing and quantification. PLoS Comput. Biol. https://doi.org/10.1371/journal.pcbi.1006018 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Chawade, A., Alexandersson, E. & Levander, F. Normalyzer: a tool for rapid evaluation of normalization methods for omics data sets. J. Proteome Res. https://doi.org/10.1021/pr401264n (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Wang, S. et al. MetaboGroup S: a group entropy-based web platform for evaluating normalization methods in blood metabolomics data from maintenance hemodialysis patients. Anal. Chem. https://doi.org/10.1021/acs.analchem.8b03065 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Xia, J., Sinelnikov, I. V., Han, B. & Wishart, D. S. MetaboAnalyst 3.0—making metabolomics more meaningful. Nucleic Acids Res. 43, W251–W257 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Giacomoni, F. et al. Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics. Bioinformatics https://doi.org/10.1093/bioinformatics/btu813 (2015).

    Article  PubMed  Google Scholar 

  31. Wen, B., Mei, Z., Zeng, C. & Liu, S. metaX: a flexible and comprehensive software for processing metabolomics data. BMC Bioinformatics https://doi.org/10.1186/s12859-017-1579-y (2017).

  32. Cardoso, S., Afonso, T., Maraschin, M. & Rocha, M. WebSpecmine: a website for metabolomics data analysis and mining. Metabolites https://doi.org/10.3390/metabo9100237 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Sud, M. et al. Metabolomics Workbench: an international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Res. https://doi.org/10.1093/nar/gkv1042 (2016).

  34. Bictash, M. et al. Opening up the ‘Black Box’: metabolic phenotyping and metabolome-wide association studies in epidemiology. J. Clin. Epidemiol. 63, 970–979 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Cloarec, O. et al. Evaluation of the orthogonal projection on latent structure model limitations caused by chemical shift variability and improved visualization of biomarker changes in 1H NMR spectroscopic metabonomic studies. Anal. Chem. 77, 517–526 (2005).

    Article  CAS  PubMed  Google Scholar 

  36. Trygg, J., Holmes, E. & Lundstedt, T. Chemometrics in metabonomics. J. Proteome Res. 6, 469–479 (2007).

    Article  CAS  PubMed  Google Scholar 

  37. Tzoulaki, I., Ebbels, T. M. D., Valdes, A., Elliott, P. & Ioannidis, J. P. A. Design and analysis of metabolomics studies in epidemiologic research: a primer on -omic technologies. Am. J. Epidemiol. 180, 129–139 (2014).

    Article  PubMed  Google Scholar 

  38. Ren, S., Hinzman, A. A., Kang, E. L., Szczesniak, R. D. & Lu, L. J. Computational and statistical analysis of metabolomics data. Metabolomics 11, 1492–1513 (2015).

    Article  CAS  Google Scholar 

  39. Xia, J., Psychogios, N., Young, N. & Wishart, D. S. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 37, W652–W660 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Gromski, P. S. et al. A tutorial review: metabolomics and partial least squares-discriminant analysis—a marriage of convenience or a shotgun wedding. Anal. Chim. Acta 879, 10–23 (2015).

    Article  CAS  PubMed  Google Scholar 

  41. Smilde, A. K. et al. Dynamic metabolomic data analysis: a tutorial review. Metabolomics 6, 3–17 (2010).

    Article  CAS  PubMed  Google Scholar 

  42. Beckonert, O. et al. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat. Protoc. 2, 2692–2703 (2007).

    Article  CAS  PubMed  Google Scholar 

  43. Beckonert, O. et al. High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues. Nat. Protoc. 5, 1019–1032 (2010).

    Article  CAS  PubMed  Google Scholar 

  44. Southam, A. D., Weber, R. J. M., Engel, J., Jones, M. R. & Viant, M. R. A complete workflow for high-resolution spectral-stitching nanoelectrospray direct-infusion mass-spectrometry-based metabolomics and lipidomics. Nat. Protoc. 12, 255–273 (2017).

    Article  CAS  Google Scholar 

  45. Dunn, W. B. et al. Metabolic profiling of serum using ultra performance liquid chromatography and the LTQ-Orbitrap mass spectrometry system. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 871, 288–298 (2008).

    Article  CAS  Google Scholar 

  46. Want, E. J. et al. Global metabolic profiling procedures for urine using UPLC-MS. Nat. Protoc. 5, 1005–1018 (2010).

    Article  CAS  PubMed  Google Scholar 

  47. Want, E. J. et al. Global metabolic profiling of animal and human tissues via UPLC-MS. Nat. Protoc. 8, 17–32 (2013).

    Article  CAS  PubMed  Google Scholar 

  48. Dona, A. C. et al. Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping. Anal. Chem. 86, 9887–9894 (2014).

    Article  CAS  PubMed  Google Scholar 

  49. Dunn, W. B. et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 6, 1060–1083 (2011).

    Article  CAS  PubMed  Google Scholar 

  50. Jiménez, B. et al. Quantitative lipoprotein subclass and low molecular weight metabolite analysis in human serum and plasma by 1H NMR spectroscopy in a multilaboratory trial. Anal. Chem. https://doi.org/10.1021/acs.analchem.8b02412 (2018).

    Article  PubMed  Google Scholar 

  51. Broadhurst, D. et al. Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics https://doi.org/10.1007/s11306-018-1367-3 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Mahieu, N. G. & Patti, G. J. Systems-level annotation of a metabolomics data set reduces 25 000 features to fewer than 1000 unique metabolites. Anal. Chem. 89, 10397–10406 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Craig, A., Cloarec, O., Holmes, E., Nicholson, J. K. & Lindon, J. C. Scaling and normalization effects in NMR spectroscopic metabonomic data sets. Anal. Chem. 78, 2262–2267 (2006).

    Article  CAS  PubMed  Google Scholar 

  54. Johansson, E., Wold, S. & Sjödin, K. Minimizing effects of closure on analytical data. Anal. Chem. 56, 1685–1688 (1984).

    Article  Google Scholar 

  55. Chayes, F. & Trochimczyk, J. An effect of closure on the structure of principal components. J. Int. Assoc. Math. Geol. 10, 323–333 (1978).

    Article  CAS  Google Scholar 

  56. Rietjens, M. Reduction of error propagation due to normalization: {Effect} of error propagation and closure on spurious correlations. Anal. Chim. Acta 316, 205–215 (1995).

    Article  CAS  Google Scholar 

  57. Saccenti, E. Correlation patterns in experimental data are affected by normalization procedures: consequences for data analysis and network inference. J. Proteome Res. 16, 619–634 (2017).

    Article  CAS  PubMed  Google Scholar 

  58. Kohl, S. M. et al. State-of-the art data normalization methods improve NMR-based metabolomic analysis. Metabolomics 8, 146–160 (2012).

    Article  CAS  PubMed  Google Scholar 

  59. Wu, Y. & Li, L. Sample normalization methods in quantitative metabolomics. J. Chromatogr. A 1430, 80–95 (2016).

    Article  CAS  PubMed  Google Scholar 

  60. Van Der Kloet, F. M., Bobeldijk, I., Verheij, E. R. & Jellema, R. H. Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping. J. Proteome Res. 8, 5132–5141 (2009).

    Article  PubMed  CAS  Google Scholar 

  61. Berg, R. A., van den, Hoefsloot, H. C. J., Westerhuis, J. A., Smilde, A. K. & van der Werf, M. J. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7, 142 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Rocke, D. M. & Durbin, B. Approximate variance-stabilizing transformations for gene-expression microarray data. Bioinformatics 19, 966–972 (2003).

    Article  CAS  PubMed  Google Scholar 

  63. Purohit, P. V., Rocke, D. M., Viant, M. R. & Woodruff, D. L. Discrimination models using variance-stabilizing transformation of metabolomic NMR data. OMICS 8, 118–130 (2004).

    Article  CAS  PubMed  Google Scholar 

  64. Bro, R. & Smilde, A. K. Principal component analysis. Anal. Methods 6, 2812–2831 (2014).

    Article  CAS  Google Scholar 

  65. Wold, S., Esbensen, K. & Geladi, P. Principal component analysis. Chemom. Intell. Lab. Syst. 2, 37–52 (1987).

    Article  CAS  Google Scholar 

  66. Geladi, P. & Kowalski, B. R. Partial least-squares regression: a tutorial. Anal. Chim. Acta 185, 1–17 (1986).

    Article  CAS  Google Scholar 

  67. Wold, S. et al. PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. Syst. 58, 109–130 (2001).

    Article  CAS  Google Scholar 

  68. Barker, M. & Rayens, W. Partial least squares for discrimination. J. Chemom. 17, 166–173 (2003).

    Article  CAS  Google Scholar 

  69. Trygg, J. & Wold, S. Orthogonal projections to latent structures (O-PLS). J. Chemom. 16, 119–128 (2002).

    Article  CAS  Google Scholar 

  70. Wiklund, S. et al. Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Anal. Chem. 80, 115–122 (2008).

    Article  CAS  PubMed  Google Scholar 

  71. Bylesjo, M. et al. OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. J. Chemom. 20, 341–351 (2006).

    Article  CAS  Google Scholar 

  72. Wold, S., Antti, H., Lindgren, F. & Öhman, J. Orthogonal signal correction of near-infrared spectra. Chemom. Intell. Lab. Syst. 44, 175–185 (1998).

    Article  CAS  Google Scholar 

  73. Fearn, T. On orthogonal signal correction. Chemom. Intell. Lab. Syst. 50, 47–52 (2000).

    Article  CAS  Google Scholar 

  74. Szymańska, E., Saccenti, E., Smilde, A. K. & Westerhuis, J. A. Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies. Metabolomics 8, 3–16 (2012).

    Article  PubMed  CAS  Google Scholar 

  75. Triba, M. N. et al. PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters. Mol. BioSyst. 11, 13–19 (2015).

    Article  CAS  PubMed  Google Scholar 

  76. MacGregor, J. F. & Kourti, T. Statistical process control of multivariate processes. Control Eng. Pract. 3, 403–414 (1995).

    Article  Google Scholar 

  77. Mahalanobis, P. C. On the generalized distance in statistics. Proc. Natl Inst. Sci. India 2, 49–55 (1936).

    Google Scholar 

  78. Eriksson, L., Byrne, T., Johansson, E., Trygg, J. & Vikström, C. Multi- and Megavariate Data Analysis: Basic Principles and Applications (Umetrics Academy, 2013).

  79. Martens, H. & Naes, T. Multivariate Calibration (John Wiley & Sons, 1989).

  80. Hastie, T., Tibshirani, R. & Friedman, J. The elements of statistical learning. Elements 1, 337–387 (2009).

    Google Scholar 

  81. Broadhurst, D. I. D. I. & Kell, D. B. Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2, 171–196 (2006).

    Article  CAS  Google Scholar 

  82. Varma, S. et al. Bias in error estimation when using cross-validation for model selection. BMC Bioinforma. 7, 91 (2006).

    Article  CAS  Google Scholar 

  83. Burman, P. A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika 76, 503–514 (1989).

    Article  Google Scholar 

  84. Lindgren, F., Hansen, B., Karcher, W., Sjöström, M. & Eriksson, L. Model validation by permutation tests: applications to variable selection. J. Chemom. 10, 521–532 (1996).

    Article  CAS  Google Scholar 

  85. van der Voet, H. Comparing the predictive accuracy of models using a simple randomization test. Chemom. Intell. Lab. Syst. 25, 313–323 (1994).

    Article  Google Scholar 

  86. Efron, B. Bootstrap methods: another look at the jackknife. Ann. Stat. 7, 1–26 (1979).

    Article  Google Scholar 

  87. Zweig, M. H. & Campbell, G. Receiver-operating characteristics (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 39, 561–577 (1993).

    Article  CAS  PubMed  Google Scholar 

  88. de Jong, S. SIMPLS: an alternative approach to partial least squares regression. Chemom. Intell. Lab. Syst. 18, 251–263 (1993).

    Article  Google Scholar 

  89. Galindo-Prieto, B., Eriksson, L. & Trygg, J. Variable influence on projection (VIP) for orthogonal projections to latent structures (OPLS). J. Chemom. 28, 623–632 (2014).

    Article  CAS  Google Scholar 

  90. Chong, I.-G. & Jun, C.-H. Performance of some variable selection methods when multicollinearity is present. Chemom. Intell. Lab. Syst. 78, 103–112 (2005).

    Article  CAS  Google Scholar 

  91. Frank, I. E. & Friedman, J. H. A statistical view of some chemometrics regression tools. Technometrics 35, 109–135 (1993).

    Article  Google Scholar 

  92. Krämer, N. An overview on the shrinkage properties of partial least squares regression. Comput. Stat. 22, 249–273 (2007).

    Article  Google Scholar 

  93. Abdi, H. H. The Bonferonni and Šidák corrections for multiple comparisons. Encycl. Meas. Stat. 1, 1–9 (2007).

    Google Scholar 

  94. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing on JSTOR. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  95. Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under depencency. Ann. Stat. 29, 1165–1188 (2001).

    Article  Google Scholar 

  96. Ferreira, J. A. & Zwinderman, A. Approximate power and sample size calculations with the Benjamini–Hochberg method. Int. J. Biostat. https://doi.org/10.2202/1557-4679.1018 (2006).

  97. Nyamundanda, G., Gormley, I. C., Fan, Y., Gallagher, W. M. & Brennan, L. MetSizeR: selecting the optimal sample size for metabolic studies using an analysis based approach. BMC Bioinforma. 14, 338–345 (2013).

    Article  Google Scholar 

  98. Jung, S.-H. & Young, S. S. Power and sample size calculation for microarray studies. J. Biopharm. Stat. 22, 30–42 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  99. Ferreira, J. A. & Zwinderman, A. Approximate sample size calculations with microarray data: an illustration. Stat. Appl. Genet. Mol. Biol. 5, Article25 (2006).

    Article  PubMed  Google Scholar 

  100. Jung, S.-H., Bang, H. & Young, S. Sample size calculation for multiple testing in microarray data analysis. Biostatistics 6, 157–169 (2005).

    Article  PubMed  Google Scholar 

  101. Blaise, B. J. et al. Power analysis and sample size determination in metabolic phenotyping. Anal. Chem. 88, 5179–5188 (2016).

    Article  CAS  PubMed  Google Scholar 

  102. Billoir, E., Navratil, V. & Blaise, B. J. Sample size calculation in metabolic phenotyping studies. Brief. Bioinform. 16, 813–819 (2014).

    Article  Google Scholar 

  103. Blaise, B. J. Data-driven sample size determination for metabolic phenotyping studies. Anal. Chem. 85, 8943–8950 (2013).

    Article  CAS  PubMed  Google Scholar 

  104. Continuum Analytics. Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Continuum Analytics, Nov. 2016. https://continuum.io (2016).

  105. R Core Team & Team, R. C. R: A Language and Environment for Statistical Computing (2017).

  106. Pedregosa, F., Grisel, O., Weiss, R., Passos, A. & Brucher, M. Scikit-learn: Machine Learning in Python. 12, 2825–2830 (2011).

  107. Kluyver, T. et al. Jupyter Notebooks—a publishing format for reproducible computational workflows. Positioning and Power in Academic Publishing: Players, Agents and Agendas https://doi.org/10.3233/978-1-61499-649-1-87 (2016).

  108. Blaise, B. J. et al. Metabolic profiling strategy of Caenorhabditis elegans by whole-organism nuclear magnetic resonance. J. Proteome Res. 8, 2542–2550 (2009).

    Article  CAS  PubMed  Google Scholar 

  109. Blaise, B. J. et al. Metabotyping of Caenorhabditis elegans reveals latent phenotypes. Proc. Natl Acad. Sci. USA. 104, 19808–19812 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. https://doi.org/10.1111/j.1467-9868.2005.00503.x (2005).

    Article  Google Scholar 

  111. Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B https://doi.org/10.1111/j.2517-6161.1996.tb02080.x (1996).

    Article  Google Scholar 

  112. Hoerl, A. E. & Kennard, R. W. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12, 55–67 (1970).

    Article  Google Scholar 

  113. Breiman, L. Random forests. Mach. Learn. https://doi.org/10.1023/A:1010933404324 (2001).

    Article  Google Scholar 

  114. Sangster, T., Major, H., Plumb, R., Wilson, A. J. & Wilson, I. D. A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS-based metabonomic analysis. Analyst 131, 1075–1078 (2006).

    Article  CAS  PubMed  Google Scholar 

  115. Sands, C. J. et al. The nPYc-Toolbox, a Python module for the pre-processing, quality-control and analysis of metabolic profiling datasets. Bioinformatics https://doi.org/10.1093/bioinformatics/btz566 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  116. Kamleh, M. A., Ebbels, T. M. D., Spagou, K., Masson, P. & Want, E. J. Optimizing the use of quality control samples for signal drift correction in large-scale urine metabolic profiling studies. Anal. Chem. 84, 2670–2677 (2012).

    Article  CAS  PubMed  Google Scholar 

  117. Wehrens, R. et al. Improved batch correction in untargeted MS-based metabolomics. Metabolomics 12, 88 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  118. Mehmood, T., Liland, K. H., Snipen, L. & Sæbø, S. A review of variable selection methods in partial least squares regression. Chemom. Intell. Lab. Syst. 118, 62–69 (2012).

    Article  CAS  Google Scholar 

  119. Lê Cao, K.-A., Boitard, S. & Besse, P. Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinforma. 12, 253 (2011).

    Article  Google Scholar 

  120. Cloarec, O. et al. Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Anal. Chem. 77, 1282–1289 (2005).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

B.J.B. is supported by the Analytical Chemistry Trust Fund (Tom West Analytical Fellowship) and the Fondation Bettencourt Schueller. G.C. is supported by the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre (BRC). T.E. acknowledges support from the EU COSMOS project (grant agreement 312941), the EU PhenoMeNal project (Project reference: 654241), UK BBSRC grant BB/T007974/1 and NIH grant R01 HL133932-01. E.H. is supported by the Department of Jobs, Tourism, Science and Innovation, Government of Western Australian Government through the Premier’s Science Fellowship Program. This work was supported by the Medical Research Council (MRC) and National Institute for Health Research (NIHR) (grant number MC_PC_12025) and the MRC UK Consortium for MetAbolic Phenotyping (MAP/UK) (grant number MR/S010483/1). The Division of Systems Medicine is funded by grants from the MRC, BBSRC and NIHR, an Integrative Mammalian Biology (IMB) Capacity Building Award and an FP7- HEALTH- 2009- 241592 EuroCHIP grant and is supported by the NIHR Biomedical Research Centre Funding Scheme. The views expressed are those of the authors and not necessarily those of the (name of funder), the NHS, the NIHR or the Department of Health. AddNeuroMed was supported by the Innovative Medicines Initiative (IMI) Joint Undertaking under EMIF grant agreement, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. We thank the clinical leads for the consortium S. Lovestone (PI), H. Soininen, P. Mecocci, M. Tsolaki, B. Vellas and I. Kłoszewska for kind access to the data.

Author information

Author notes

  1. These authors contributed equally: Benjamin J. Blaise, Gonçalo D. S. Correia, Gordon A. Haggart.

Authors and Affiliations

  1. Division of Systems Medicine, Department of Metabolism, Digestion & Reproduction, Faculty of Medicine, Imperial College London, London, UK

    Benjamin J. Blaise, Gonçalo D. S. Correia, Gordon A. Haggart, Caroline Sands, Matthew R. Lewis, Jake T. M. Pearce, Elaine Holmes & Timothy M. D. Ebbels

  2. Department of Paediatric Anaesthetics, Evelina London Children’s Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London, UK

    Benjamin J. Blaise

  3. Centre for the Developing Brain, King’s College London, London, UK

    Benjamin J. Blaise

  4. National Phenome Centre, Department of Metabolism, Digestion & Reproduction, Faculty of Medicine, Imperial College London, London, UK

    Gonçalo D. S. Correia, Gordon A. Haggart, Caroline Sands, Matthew R. Lewis & Jake T. M. Pearce

  5. Computational Life Science Cluster, Department of Chemistry, Umeå University, Umeå, Sweden

    Izabella Surowiec & Johan Trygg

  6. Sartorius Corporate Research, Sartorius Stedim Data Analytics, Umeå, Sweden

    Izabella Surowiec & Johan Trygg

  7. Australian National Phenome Centre, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia

    Jeremy K. Nicholson

  8. Institute of Global Health Innovation, Imperial College London, London, UK

    Jeremy K. Nicholson

  9. Centre for Computational & Systems Medicine Institute of Health Futures, Murdoch University, Perth, Western Australia, Australia

    Elaine Holmes

Authors
  1. Benjamin J. Blaise
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gonçalo D. S. Correia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gordon A. Haggart
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Izabella Surowiec
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Caroline Sands
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Matthew R. Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jake T. M. Pearce
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Johan Trygg
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jeremy K. Nicholson
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Elaine Holmes
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Timothy M. D. Ebbels
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Timothy M. D. Ebbels.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks the anonymous reviewers for their contribution to the peer review of this work.

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

Related links

Key reference using this protocol

Holmes, E. et al. Nature 453, 396–400 (2008): https://doi.org/10.1038/nature06882

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2 and Supplementary Procedures 1.1 and 1.2.

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Blaise, B.J., Correia, G.D.S., Haggart, G.A. et al. Statistical analysis in metabolic phenotyping. Nat Protoc 16, 4299–4326 (2021). https://doi.org/10.1038/s41596-021-00579-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-021-00579-1

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research