A toolbox to explore NMR metabolomic data sets using the R environment

Chemometrics and Intelligent Laboratory Systems

Volumn 126, Issue , 2013, Pages 50-59

  Balayssac, Stéphane ^a   Déjean, Sébastien ^a   Lalande, Julie ^a   Gilard, Véronique ^a   Malet Martino, Myriam ^a  

^a UNIVERSITÉ DE TOULOUSE   (France)

Author keywords

Graphical and statistical tools; Metabolomic; NMR

Indexed keywords

4 AMINOBUTYRIC ACID; ASPARTIC ACID; CREATININE; GLUTAMIC ACID; GLUTAMINE; N ACETYLASPARTIC ACID; TAURINE;

ALZHEIMER DISEASE; ANIMAL TISSUE; ARTICLE; BIVARIATE ANALYSIS; CONTROLLED STUDY; DATA ANALYSIS SOFTWARE; MATHEMATICAL COMPUTING; METABOLITE; METABOLOMICS; MOUSE; NONHUMAN; PRIORITY JOURNAL; PROTON NUCLEAR MAGNETIC RESONANCE; STATISTICAL MODEL; UNIVARIATE ANALYSIS;

EID: 84878417472     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2013.04.015     Document Type: Article

References (47)

1. Antoniadis A., Bigot J., Lambert-Lacroix S., Letué F. Nonparametric pre-processing methods and inference tools for analyzing time-of-flight mass spectrometry data. Current Analytical Chemistry 2007, 3:127-147.
2. Li X., Li J., Yao X. A wavelet-based data pre-processing analysis approach in mass spectrometry. Computers in Biology and Medicine 2007, 37:509-516.
3. Sauve A.C., Speed T. Normalization, baseline correction and alignment of high throughput mass spectrometry data. Proceedings of the Genomic Signal Processing and Statistics Workshop 2004, 26-27.
4. Yu W., Wu B., Lin N., Stone K., Williams K., Zhao H. Detecting and aligning peaks in analyzing MALDI mass spectrometry data. Computational Biology and Chemistry 2006, 30:27-38.
5. Rousseau R., Govaerts B., Verleysen M., Boulanger B. Comparison of some chemometric tools for metabonomics biomarker identification. Chemometrics and Intelligent Laboratory Systems 2007, 91:54-56.
6. Cloarec O., Dumas M.E., Craig A., Barton R.H., Trygg J., Hudson J., Blancher C., Gauguier D., Lindon J.C., Holmes E., Nicholson J. Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Analytical Chemistry 2005, 77:1282-1289.
7. Camacho D., de la Fuente A., Mendes P. The origin of correlations in metabolomics data. Metabolomics 2005, 1:53-63.
8. Steuer R. On the analysis and interpretation of correlations in metabolomic data. Briefings in Bioinformatics 2006, 7:151-158.
9. Müller-Linow M., Weckwerth W., Hütt M.T. Consistency analysis of metabolic correlation networks. BMC Systems Biology 2007, 1:44.
10. Allen E., Moing A., Ebbels T.M.D., Maucourt M., Tomos A.D., Rolin D., Hooks M.A. Correlation Network Analysis reveals a sequential reorganization of metabolic and transcriptional states during germination and gene-metabolite relationships in developing seedlings of Arabidopsis. BMC Systems Biology 2010, 4:62.
11. Sato S., Arita M., Soga T., Nishioka T., Tomita M. Time-resolved metabolomics reveals metabolic modulation in rice foliage. BMC Systems Biology 2008, 2:51.
12. R Development Core Team R Foundation for Statistical Computing, Vienna, Austria 2012, (URL http://www.R-project.org).
13. Kastenmüller G., Römisch-Margl W., Wägele B., Altmaier E., Suhre K. metaP-Server: a web-based metabolomics data analysis tool. Journal of Biomedicine and Biotechnology 2011, 7. 10.1155/2011/839862.
14. Izquierdo-García J.L., Rodríguez I., Kyriazis A., Villa P., Barreiro P., Desco M., Ruiz-Cabello M.J. A novel R-package graphic user interface for the analysis of metabonomic profiles. BMC Bioinformatics 2009, 10:363.
15. Gaude E., Chignola F., Spiliotopoulos D., Mari S., Spitaleri A., Ghitti M. MUMA: metabolomic univariate and multivariate analysis, R package http://cran.r-project.org/web/packages/muma/.
16. Izquierdo-García J.L., Villa P., Kyriazis A., Puerto-Nevado L., Pérez-Rial S., Rodríguez I., Hernandez N., Ruiz-Cabello J. Descriptive review of current NMR-based metabolomic data analysis packages. Progress in Nuclear Magnetic Resonance Spectroscopy 2011, 59:263-270.
17. Eisen M.B., Spellman P.T., Brown P.O., Botstein D. Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences of the United States of America 1998, 95:14863-14868.
18. Weckwerth W., Ehlers Loureiro M., Wenzel K., Fiehn O. Differential metabolic networks unravel the effects of silent plant phenotypes. Proceedings of the National Academy of Sciences of the United States of America 2004, 101:7809-7814.
19. Fiehn O. Metabolic networks of Cucurbita maxima phloem. Phytochemistry 2003, 62:875-886.
20. Holmes E., Cloarec O., Nicholson J.K. Probing latent biomarker signatures and in vivo pathway activity in experimental disease states via statistical total correlation spectroscopy (STOCSY) of biofluids: application to HgCl2 toxicity. Journal of Proteome Research 2006, 5:1313-1320.
21. Nielsen N.P.V., Carstensen J.M., Smedsgaard J. Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping. Journal of Chromatography. A 1998, 805:17-35.
22. Veselkov K.A., Lindon J.C., Ebbels T.M.D., Crockford D., Volynkin V.V., Holmes E., Davies D.B., Nicholson J.K. Recursive segment-wise peak alignment of biological 1H NMR spectra for improved metabolic biomarker recovery. Analytical Chemistry 2009, 81:56-66.
23. Lê Cao K.-A., González I., Déjean S. mixOmics: an R package to unravel relationships between two omics data sets. Bioinformatics 2009, 25:2855-2856. (NOTE: the package 'integrOmics' has been renamed 'mixOmics').
24. Zou H., Hastie T., Tibshirani R. Sparse principal component analysis. Journal of Computational and Graphical Statistics 2006, 15:262-286.
25. Lebart L., Piron M., Morineau A. Statistique exploratoire multidimensionnelle: Visualisations et inférences en fouille de données 2006, Dunod. 4ème édition.
26. Trygg J. O2-PLS for qualitative and quantitative analysis in multivariate calibration. Journal of Chemometrics 2002, 16:283-293.
27. Lê Cao K.-A., Boitard S., Besse P. Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinformatics 2011, 12:253.
28. Suter H.P. xlsReadWrite: Read and Write Excel files (.xls), R Package Version 1.5.4 2011.
29. Csardi G., Nepusz T. The igraph software package for complex network research. InterJournal, Complex Systems 2006, 1695.
30. Beckonert O., Keun H.C., Ebbels T.M.D., Bundy J., Holmes E., Lindon J.C., Nicholson J.K. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nature Protocols 2007, 2:2692-2703.
31. Robert O., Sabatier J., Desoubzdanne D., Lalande J., Balayssac S., Gilard V., Martino R., Malet-Martino M. pH optimization for a reliable quantification of brain tumor cell and tissue extracts with 1H MRS: focus on choline-containing-compounds and taurine. Analytical and Bioanalytical Chemistry 2011, 399:987-999.
32. Couto-Alves A., Rantalainen M., Holmes E., Nicholson J.K., Ebbels T.M.D. Analytic properties of statistical total correlation spectroscopy based information recovery in 1H NMR metabolic data sets. Analytical Chemistry 2009, 81:2075-2084.
33. Mattson M.P. Pathways towards and away from Alzheimer's disease. Nature 2004, 430:631-639.
34. Lipton S.A., Rosenberg P.A. Mechanisms of disease: excitatory amino acids as a final common pathway for neurologic disorders. The New England Journal of Medicine 1994, 330:613-622.
35. Rubin Y., LaPlaca M.C., Smith D.H., Thibault L.E., Lenkinski R.E. The effect of N-acetylaspartate on the intracellular free calcium concentration in Ntera2-neurons. Neuroscience Letters 1995, 198:209-212.
36. El Idrissi A., Trenkner E. Growth factors and taurine protect against excitotoxicity by stabilizing calcium homeostasis and energy metabolism. Journal of Neuroscience 1999, 19:9459-9468.
37. Paula-Lima A.C., De Felice F.G., Brito-Moreira J., Ferreira S.T. Activation of GABAA receptors by taurine and muscimol blocks the neurotoxicity of beta-amyloid in rat hippocampal and cortical neurons. Neuropharmacology 2005, 49:1140-1148.
38. Koga Y., Takahashi H., Oikawa D., Tachibana T., Denbow D.M., Furuse M. Brain creatine functions to attenuate stress responses through GABAnergic system in chicks. Neuroscience 2005, 132:65-71.
39. Reed D.J. Regulation of reductive processes by glutathione. Biochemical Pharmacology 1986, 35:7-13.
40. Kim C., Park E., Quinn M.R., Schuller-Levis G. The production of superoxide anion and nitric oxide by cultured murine leukocytes and the accumulation of TNF-alpha in the conditioned media is inhibited by taurine chloramine. Immunopharmacology 1996, 34:89-95.
41. Sullivan P.G., Geiger J.D., Mattson M.P., Scheff S.W. Dietary supplement creatine protects against traumatic brain injury. Annals of Neurology 2000, 48:723-729.
42. Pederzolli C.D., Mescka C.P., Scapin F., Rockenbach F.J., Sgaravatti A.M., Sgarbi M.B., Wyse A.T.S., Wannmacher C.M.D., Wajner M., Dutra-Filho C.S. N-acetylaspartic acid promotes oxidative stress in cerebral cortex of rats. International Journal of Developmental Neuroscience 2007, 25:317-324.
43. Caraux G., Pinloche S. PermutMatrix: a graphical environment to arrange gene expression profiles in optimal linear order. Bioinformatics 2005, 21:1280-1281.
44. Goldstein F.B. Biosynthesis of N-acetyl-l-aspartic acid. Biochimica et Biophysica Acta 1959, 33:583-584.
45. Choi D.W. Methods for antagonizing glutamate neurotoxicity. Cerebrovascular and Brain Metabolism Reviews 1990, 2:105-147.
46. Numata J., Ebenhöh O., Knapp E.W. Measuring correlations in metabolomic networks with mutual information. Genome Informatics 2008, 20:112-122.
47. Mardia K.V., Kent J.T., Bibby J.M. Multivariate Analysis 1979, Academic Press.