Statistical analysis of metabolomics data

Methods in Molecular Biology

Volumn 1055, Issue , 2013, Pages 291-307

  De Livera, Alysha M ^a   Olshansky, Moshe ^b   Speed, Terence P ^a  

^a UNIVERSITY OF MELBOURNE   (Australia)

^b WALTER AND ELIZA HALL INSTITUTE OF MEDICAL RESEARCH   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL FACTOR;

ARTICLE; CLUSTER ANALYSIS; CORRELATION ANALYSIS; DATA ANALYSIS; ERROR; EXPERIMENT; EXPERIMENTAL DESIGN; HYPOTHESIS; METABOLITE; METABOLOMICS; MULTIDIMENSIONAL SCALING; MULTIPLE LINEAR REGRESSION ANALYSIS; NONHUMAN; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; QUALITY CONTROL; SAMPLE; SCIENTIST; STANDARD; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICAL SIGNIFICANCE; VARIANCE;

METABOLOMICS;

EID: 84934437029     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-62703-577-4_20     Document Type: Article

References (58)

1. Fiehn O (2002) Metabolomics - the link between genotypes and phenotypes. Plant Mol Biol 48: 155-171
2. Roessner U, Bowne J (2009) What is metabolomics all about? Biotechniques 46(5): 363-365
3. Roessner U, Beckles DM (2009) Metabolite measurements. Springer, New York
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/ ac302748b
5. Glass DJ (2007) Experimental design for biologists. Cold Spring Harbor Laboratory, New York
6. Montgomery DC (2008) Design and analysis of experiments. Wiley, Hoboken
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):115
8. Schleif F-M (2007) Preprocessing of nuclear magnetic resonance spectrometry data. Technical report, August 2007
9. Katajamaa M, Orešič M (2007) Data processing for mass spectrometry-based metabolomics. J Chromatogr A 1158: 318-328
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-W660
11. Hrydziuszko O, Viant MR (2012) Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline. Metabolomics 8(1): 161-174
12. Katajamaa M, Oresic M (2005) Processing methods for differential analysis of LC/MS profile data. BMC Bioinformatics 6:179
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: 105-126
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): 6729-6736
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 7:142
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.S6-002
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 York
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): 520-525
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): 2088-2096
20. van Buuren S, Groothuis-Oudshoorn K (2011) Mice: multivariate imputation by chained equations in R. J Static Softw 45(3): 1-67
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): 231-241
22. Schlesselman J (1971) Power families: a note on the Box and Cox transformation. J R Stat Soc Ser B (Methodol) 307-311
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: 80-89
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): 283-295
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): 567-574
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): 7974-7980
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 8:93
28. Crawford LR, Morrison JD (1968) Computer methods in analytical mass spectrometry. Identification of an unknown compound in a catalog. Anal Chem 40(4): 1464-1469
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): 481848-26
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): 2447-2454
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): 21-27
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): 299-305
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): 1357-1364
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): 108-120
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): 1060-1083
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): 2670-2677
37. Gagnon-Bartsch JA, Speed TP (2011) Using control genes to correct for unwanted variation in microarray data. Biostatistics 13(3): 539-552
38. Leek JT, Storey JD (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet 3(9): 1724-1735
39. Leek JT, Storey JD (2008) A general framework for multiple testing dependence. Proc Natl Acad Sci USA 105(48): 18718-18723
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):5116
41. Efron B (2007) Correlation and large-scale simultaneous significance testing. J Am Stat Assoc 102(477): 93-103
42. Lonnstedt I, Speed TP (2002) Replicated microarray data. Stat Sin 12: 31-46
43. Smyth GK (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3(1): 1544-6115
44. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2): 65-70
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: 289-300
46. Westfall PH, Young SS (1993) Resampling-based multiple testing: examples and methods for p-value adjustment. Wiley-Interscience, New York
47. Efron B, Tibshirani R, Storey JD, Tusher V (2001) Empirical Bayes analysis of a microarray experiment. J Am Stat Assoc 96(456): 1151-1160
48. Storey JD, Tibshirani R (2001) Estimating false discovery rates under dependence, with applications to DNA microarrays. Technical report
49. Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning, 2nd edn. Springer, New York
50. Frank IE, Friedman JH (1993) A statistical view of some chemometrics regression tools. Technometrics 35(2): 109-135
51. Wold S, Sjostrom M (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58(2): 109-130
52. Vapnik V (1999) The nature of statistical learning theory. Springer, Berlin
53. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth International Group, Belmont
54. Breiman L (2001) Random forests. Mach Learn 45(1): 5-32
55. Cox TF, Cox MAA (2001) Multidimensional scaling. Chapman and Hall, Boca Raton
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 281-297
57. Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1): 59-69
58. De Livera AM, Bowne J (2013) metabolomics: A collection of functions for analysing metabolomics data. R package version 0.1.1