Advertisement

Statistical Analysis of Metabolomics Data

  • Alysha M. De Livera
  • Moshe Olshansky
  • Terence P. Speed
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1055)

Abstract

Statistical matters form an integral part of a metabolomics experiment. In this chapter we describe several important aspects in the analysis of metabolomics data such as the removal of unwanted variation and the identification of differentially abundant metabolites, along with a number of other essential statistical considerations.

Keywords

Metabolomics Data Abundant Metabolite Metabolomics Experiment Unwanted Variation Bayesian Principal Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Fiehn O (2002) Metabolomics—the link between genotypes and phenotypes. Plant Mol Biol 48:155–171PubMedCrossRefGoogle Scholar
  2. 2.
    Roessner U, Bowne J (2009) What is metabolomics all about? Biotechniques 46(5):363–365PubMedCrossRefGoogle Scholar
  3. 3.
    Roessner U, Beckles DM (2009) Metabolite measurements. Springer, New YorkGoogle Scholar
  4. 4.
    De Livera AM, Dias DA, De Souza D, Rupasinghe T, Pyke J, Tull D, Roessner U, McConville M, Speed TP (2012) Normalising and integrating metabolomics data. Anal Chem 84(24):10768–10776. DOI:10.1021/ac302748bGoogle Scholar
  5. 5.
    Glass DJ (2007) Experimental design for biologists. Cold Spring Harbor Laboratory, New YorkGoogle Scholar
  6. 6.
    Montgomery DC (2008) Design and analysis of experiments. Wiley, HobokenGoogle Scholar
  7. 7.
    O’Callaghan S, Desouza DP, Isaac A, Wang Q, Hodkinson L, Olshansky M, Erwin T, Appelbe B, Tull DL, Roessner U, Bacic A, McConville MJ, Likic VA (2012) PyMS: a Python toolkit for processing of gas chromatography–mass spectrometry (GC–MS) data. Application and comparative study of selected tools. BMC Bioinformatics 13(1):115Google Scholar
  8. 8.
    Schleif F-M (2007) Preprocessing of nuclear magnetic resonance spectrometry data. Technical report, August 2007Google Scholar
  9. 9.
    Katajamaa M, Orešič M (2007) Data processing for mass spectrometry-based metabolomics. J Chromatogr A 1158:318–328PubMedCrossRefGoogle Scholar
  10. 10.
    Xia J, Psychogios N, Young N, Wishart DS (2009) MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res 37:W652–W660PubMedCrossRefGoogle Scholar
  11. 11.
    Hrydziuszko O, Viant MR (2012) Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline. Metabolomics 8(1):161–174CrossRefGoogle Scholar
  12. 12.
    Katajamaa M, Oresic M (2005) Processing methods for differential analysis of LC/MS profile data. BMC Bioinformatics 6:179PubMedCrossRefGoogle Scholar
  13. 13.
    Steuer R, Morgenthal K, Weckwerth W, Selbig J (2007) A gentle guide to the analysis of metabolomic data. Methods Mol Biol (Clifton, NJ) 358:105–126Google Scholar
  14. 14.
    Smilde AK, van der Werf MJ, Bijlsma S, van der Werff-van der Vat BJC, Jellema RH (2005) Fusion of mass spectrometry-based metabolomics data. Anal Chem 77(20):6729–6736Google Scholar
  15. 15.
    van den Berg RA, Hoefsloot HCJ, Westerhuis JA, Smilde AK, van der Werf MJ (2006) Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7:142PubMedCrossRefGoogle Scholar
  16. 16.
    Temmerman L, De Livera AM, Bowne J, Sheedy RJ, Callahan DL, Nahid A, De Souza DP, Schoofs L, Tull DL, McConville JM, Roessner U, Wentworth JM (2012) Cross-platform urine metabolomics of experimental hyperglycemia in type 2 diabetes. Diab Metab vol S6:002. DOI:10.4172/2155-6156.S6-002Google Scholar
  17. 17.
    Roessner U, Nahid A, Chapman B, Hunter A, Bellgard M (2011) Metabolomics—the combination of analytical biochemistry, biology, and informatics, vol 1, 2nd edn. Elsevier B.V., New YorkGoogle Scholar
  18. 18.
    Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB (2001) Missing value estimation methods for DNA microarrays. Bioinformatics (Oxford, England) 17(6):520–525CrossRefGoogle Scholar
  19. 19.
    Oba S, Sato M, Takemasa I, Monden M, Matsubara K, Ishii S (2003) A Bayesian missing value estimation method for gene expression profile data. Bioinformatics 19(16):2088–2096PubMedCrossRefGoogle Scholar
  20. 20.
    van Buuren S, Groothuis-Oudshoorn K (2011) Mice: multivariate imputation by chained equations in R. J Static Softw 45(3):1–67Google Scholar
  21. 21.
    Goodacre R, Broadhurst D, Smilde AK, Kristal BS, Baker JD, Beger R, Bessant C, Connor S, Capuani G, Craig A, Ebbels T, Kell DB, Manetti C, Newton J, Paternostro G, Somorjai R, Sjöström M, Trygg J, Wulfert F (2007) Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics 3(3):231–241CrossRefGoogle Scholar
  22. 22.
    Schlesselman J (1971) Power families: a note on the Box and Cox transformation. J R Stat Soc Ser B (Methodol) 307–311Google Scholar
  23. 23.
    Callahan DL, Roessner U, Dumontet V, De Livera AM, Doronila A, Baker AJM, Kolev SD (2012) Elemental and metabolite profiling of nickel hyperaccumulators from New Caledonia. Phytochemistry 81:80–89PubMedCrossRefGoogle Scholar
  24. 24.
    Gullberg J, Jonsson P, Nordström A, Sjöström M, Moritz T (2004) Design of experiments: an efficient strategy to identify factors influencing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry. Anal Biochem 331(2):283–295PubMedCrossRefGoogle Scholar
  25. 25.
    Bijlsma S, Bobeldijk I, Verheij ER, Ramaker R, Kochhar S, Macdonald I, Van Ommen B, Smilde AK (2006) Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation. Anal Chem 78(2):567–574PubMedCrossRefGoogle Scholar
  26. 26.
    Redestig H, Fukushima A, Stenlund H, Moritz T, Arita M, Saito K, Kusano M (2009) Compensation for systematic cross-contribution improves normalization of mass spectrometry based metabolomics data. Anal Chem 81(19):7974–7980PubMedCrossRefGoogle Scholar
  27. 27.
    Sysi-Aho M, Katajamaa M, Laxman Y, Oresic M (2007) Normalization method for metabolomics data using optimal selection of multiple internal standards. BMC Bioinformatics 8:93PubMedCrossRefGoogle Scholar
  28. 28.
    Crawford LR, Morrison JD (1968) Computer methods in analytical mass spectrometry. Identification of an unknown compound in a catalog. Anal Chem 40(4):1464–1469Google Scholar
  29. 29.
    Wang W, Zhou H, Lin H, Roy S, Shaler TA, Hill LR, Norton S, Kumar P, Anderle M, Becker CH (2003) Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. Anal Chem 75(18):481848–26CrossRefGoogle Scholar
  30. 30.
    Scholz M, Gatzek S, Sterling A, Fiehn O, Selbig J (2004) Metabolite fingerprinting: detecting biological features by independent component analysis. Bioinformatics (Oxford, England) 20(15):2447–2454CrossRefGoogle Scholar
  31. 31.
    Cairns DA, Thompson D, Perkins DN, Stanley AJ, Selby PJ, Banks RE (2008) Proteomic profiling using mass spectrometry—does normalising by total ion current potentially mask some biological differences? Proteomics 8(1):21–27PubMedCrossRefGoogle Scholar
  32. 32.
    Gika HG, Macpherson E, Theodoridis GA, Wilson ID (2008) Evaluation of the repeatability of ultra-performance liquid chromatography-TOF-MS for global metabolic profiling of human urine samples. J Chromatogr B Anal Technol Biomed Life Sci 871(2):299–305CrossRefGoogle Scholar
  33. 33.
    Zelena E, Dunn WB, Broadhurst D, Francis-McIntyre S, Carroll KM, Begley P, O’Hagan S, Knowles JD, Halsall A, Wilson ID, Kell DB (2009) Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. Anal Chem 81(4):1357–1364PubMedCrossRefGoogle Scholar
  34. 34.
    Lai L, Michopoulos F, Gika H, Theodoridis G, Wilkinson RW, Odedra R, Wingate J, Bonner R, Tate S, Wilson ID (2010) Methodological considerations in the development of HPLC-MS methods for the analysis of rodent plasma for metabolomic studies. Mol Biosyst 6(1):108–120PubMedCrossRefGoogle Scholar
  35. 35.
    Dunn WB, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N, Brown M, Knowles JD, Halsall A, Haselden JN, Nicholls AW, Wilson ID, Kell DB, Goodacre R (2011) Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc 6(7):1060–1083PubMedCrossRefGoogle Scholar
  36. 36.
    Kamleh MA, Ebbels TMD, Spagou K, Masson P, Want EJ (2012) Optimizing the use of quality control samples for signal drift correction in large-scale urine metabolic profiling studies. Anal Chem 84(6):2670–2677PubMedCrossRefGoogle Scholar
  37. 37.
    Gagnon-Bartsch JA, Speed TP (2011) Using control genes to correct for unwanted variation in microarray data. Biostatistics 13(3):539–552PubMedCrossRefGoogle Scholar
  38. 38.
    Leek JT, Storey JD (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet 3(9):1724–1735PubMedCrossRefGoogle Scholar
  39. 39.
    Leek JT, Storey JD (2008) A general framework for multiple testing dependence. Proc Natl Acad Sci USA 105(48):18718–18723PubMedCrossRefGoogle Scholar
  40. 40.
    Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98(9):5116PubMedCrossRefGoogle Scholar
  41. 41.
    Efron B (2007) Correlation and large-scale simultaneous significance testing. J Am Stat Assoc 102(477):93–103CrossRefGoogle Scholar
  42. 42.
    Lonnstedt I, Speed TP (2002) Replicated microarray data. Stat Sin 12:31–46Google Scholar
  43. 43.
    Smyth GK (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3(1):1544–6115Google Scholar
  44. 44.
    Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65–70Google Scholar
  45. 45.
    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57:289–300Google Scholar
  46. 46.
    Westfall PH, Young SS (1993) Resampling-based multiple testing: examples and methods for p-value adjustment. Wiley-Interscience, New YorkGoogle Scholar
  47. 47.
    Efron B, Tibshirani R, Storey JD, Tusher V (2001) Empirical Bayes analysis of a microarray experiment. J Am Stat Assoc 96(456):1151–1160CrossRefGoogle Scholar
  48. 48.
    Storey JD, Tibshirani R (2001) Estimating false discovery rates under dependence, with applications to DNA microarrays. Technical reportGoogle Scholar
  49. 49.
    Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning, 2nd edn. Springer, New YorkGoogle Scholar
  50. 50.
    Frank IE, Friedman JH (1993) A statistical view of some chemometrics regression tools. Technometrics 35(2):109–135CrossRefGoogle Scholar
  51. 51.
    Wold S, Sjostrom M (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58(2):109–130CrossRefGoogle Scholar
  52. 52.
    Vapnik V (1999) The nature of statistical learning theory. Springer, BerlinGoogle Scholar
  53. 53.
    Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth International Group, BelmontGoogle Scholar
  54. 54.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32Google Scholar
  55. 55.
    Cox TF, Cox MAA (2001) Multidimensional scaling. Chapman and Hall, Boca RatonGoogle Scholar
  56. 56.
    MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. University of California Press, Berkeley, pp 281–297Google Scholar
  57. 57.
    Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1):59–69CrossRefGoogle Scholar
  58. 58.
    De Livera AM, Bowne J (2013) metabolomics: A collection of functions for analysing metabolomics data. R package version 0.1.1Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Alysha M. De Livera
    • 1
  • Moshe Olshansky
    • 2
  • Terence P. Speed
    • 2
  1. 1.Metabolomics Australia, Bio21 Institute (Molecular Science and Biotechnology Institute)The University of MelbourneMelbourneAustralia
  2. 2.Bioinformatics DivisionWalter and Eliza Hall InstituteParkvilleAustralia

Personalised recommendations