Abstract
High throughput technologies like transcriptomics using DNA arrays or metabolomics employing a combination of gas chromatography with mass spectrometry provide valuable information about cellular processes. However, the measurements are often highly corrupted with noise of the experimental data which makes it sometimes difficult to draw reliable conclusions. Therefore, suitable statistical methods are needed for the evaluation of the experimental data to distinguish changes caused by biological phenomena from random variations due to noise. This paper introduces a likelihood ratio test to multiple metabolome measurements. The method was tested to differentiate differential metabolite compositions obtained from the pathogenic bacterium Pseudomonas aeruginosa grown under various environmental conditions.
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References
Joyce, A.R., Palsson, B.Ø: The model organism as a system: integrating ’omics’ data sets. Nat. Rev. Mol. Cell Biol. 7, 198–210 (2006)
Hollywood, K., Brison, D.R., Goodacre, R.: Metabolomics: current technologies and future trends. Proteomics 6, 4716–4723 (2006)
Strelkov, S., von Elstermann, M., Schomburg, D.: Comprehensive analysis of metabolites in Corynebacterium glutamicum by gas chromatography/mass spectrometry. Biol. Chem. 385(9), 853–861 (2004)
Kell, D.B.: Systems biology, metabolic modelling and metabolomics in drug discovery and development. Drug Discov. Today 11, 1085–1092 (2006)
Weckwerth, W., Morgenthal, K.: Metabolomics: from pattern recognition to biological interpretation. Drug Discov. Today 10(22), 1551–1558 (2005)
Choi, J.Y., Sifri, C.D., Goumnerov, B.C., Rahme, L.G., Ausubel, F.M., Calderwood, S.B.: Identification of virulence genes in a pathogenic strain of Pseudomonas aeruginosa by representational difference analysis. J. Bacteriol. 184, 952–961 (2002)
Förster, J., Gombert, A.K., Nielsen, J.: A functional genomics approach using metabolomics and in silico pathway analysis. Biotechn. Bioeng. 79, 703–712 (2002)
Spellman, P.T.: Cluster analysis and display. In: Bowtell, D., Sambrook, J. (eds.) DNA Microarrays, Cold Spring Harbor Lab, pp. 569–581 (2002)
Georgieva, O., Klawonn, F., Härtig, E.: Fuzzy clustering of macroarray data. In: Reusch, B. (ed.) Computational Intelligence, Theory and Application, pp. 83–94. Springer, Heidelberg (2005)
Klawonn, F., Hundertmark, C., Jänsch, L.: A maximum likelihood approach to noise estimation for intensity measurements in biology. In: Tsumoto, S., Clifton, C., Zhong, N., Wu, X., Liu, J., Wah, B., Cheung, Y.M. (eds.) Proc. 6th IEEE Intern. Conf. on Data Mining: Workshops, pp. 180–184. IEEE, Los Alamitos (2006)
Baldi, P., Long, A.D.: A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inferences of gene changes. Bioinformatics 17, 509–519 (2001)
Cui, X., Hwang, J.T.G., Qiu, J., Blades, N.J., Churchill, G.A.: Improved statistical tests for differential gene expression by shrinking variance components. Biostatistics 6, 59–75 (2005)
McLachlan, G.J., Bean, R.W., Ben-Tovim Jones, L.: A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics 22, 1608–1615 (2006)
Wilks, S.S.: The large-sample distribution of the likelihood ratio for testing composite hypotheses. Ann. Math. Statistics 9, 60–62 (1938)
Miller, R.G.J.: Simultaneous Statistical Inference. Springer, New York (1991)
Shaffer, J.P.: Multiple hypothesis testing. Ann. Rev. Psych. 46, 561–584 (1995)
Borriello, G., Werner, E., Roe, F., Kim, A.M., Ehrlich, G.D., Stewart, P.S.: Oxygen limitation contributes to antibiotic tolerance of Pseudomonas aeruginosa in biofilms. Antimicrob. Agents Chemother. 48, 2659–2664 (2004)
Cioni, P., Bramanti, E., Strambini, G.B.: Effects of sucrose on the internal dynamics of azurin. Biophys. J. 88(6), 4213–4222 (2005)
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Klawonn, F. et al. (2007). A Likelihood Ratio Test for Differential Metabolic Profiles in Multiple Intensity Measurements. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4693. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74827-4_61
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DOI: https://doi.org/10.1007/978-3-540-74827-4_61
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