A Normalized Tree Index for identification of correlated clinical parameters in microarray data

Martin C, Tauchen A, Becker A, Nattkemper TW (2011)
BioData Mining 4(1): 2.

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Zeitschriftenaufsatz | Veröffentlicht | Englisch
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Abstract / Bemerkung
BACKGROUND: Measurements on gene level are widely used to gain new insights in complex diseases e.g. cancer. A promising approach to understand basic biological mechanisms is to combine gene expression profiles and classical clinical parameters. However, the computation of a correlation coefficient between high-dimensional data and such parameters is not covered by traditional statistical methods. METHODS: We propose a novel index, the Normalized Tree Index (NTI), to compute a correlation coefficient between the clustering result of high-dimensional microarray data and nominal clinical parameters. The NTI detects correlations between hierarchically clustered microarray data and nominal clinical parameters (labels) and gives a measurement of significance in terms of an empiric p-value of the identified correlations. Therefore, the microarray data is clustered by hierarchical agglomerative clustering using standard settings. In a second step, the computed cluster tree is evaluated. For each label, a NTI is computed measuring the correlation between that label and the clustered microarray data. RESULTS: The NTI successfully identifies correlated clinical parameters at different levels of significance when applied on two real-world microarray breast cancer data sets. Some of the identified highly correlated labels confirm the actual state of knowledge whereas others help to identify new risk factors and provide a good basis to formulate new hypothesis. CONCLUSIONS: The NTI is a valuable tool in the domain of biomedical data analysis. It allows the identification of correlations between high-dimensional data and nominal labels, while at the same time a p-value measures the level of significance of the detected correlations.
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BioData Mining
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4
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1
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2
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Article Processing Charge funded by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld University.
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Martin C, Tauchen A, Becker A, Nattkemper TW. A Normalized Tree Index for identification of correlated clinical parameters in microarray data. BioData Mining. 2011;4(1):2.
Martin, C., Tauchen, A., Becker, A., & Nattkemper, T. W. (2011). A Normalized Tree Index for identification of correlated clinical parameters in microarray data. BioData Mining, 4(1), 2. doi:10.1186/1756-0381-4-2
Martin, C., Tauchen, A., Becker, A., and Nattkemper, T. W. (2011). A Normalized Tree Index for identification of correlated clinical parameters in microarray data. BioData Mining 4, 2.
Martin, C., et al., 2011. A Normalized Tree Index for identification of correlated clinical parameters in microarray data. BioData Mining, 4(1), p 2.
C. Martin, et al., “A Normalized Tree Index for identification of correlated clinical parameters in microarray data”, BioData Mining, vol. 4, 2011, pp. 2.
Martin, C., Tauchen, A., Becker, A., Nattkemper, T.W.: A Normalized Tree Index for identification of correlated clinical parameters in microarray data. BioData Mining. 4, 2 (2011).
Martin, Christian, Tauchen, Annika, Becker, Anke, and Nattkemper, Tim Wilhelm. “A Normalized Tree Index for identification of correlated clinical parameters in microarray data”. BioData Mining 4.1 (2011): 2.
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2012-02-06T14:25:32Z

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28 References

Daten bereitgestellt von Europe PubMed Central.

Breast cancer gene expression profiling: clinical trial and practice implications.
Loi S, Desmedt C, Cardoso F, Piccart M, Sotiriou C., Pharmacogenomics 6(1), 2005
PMID: 15723605
Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis.
Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, Nordgren H, Farmer P, Praz V, Haibe-Kains B, Desmedt C, Larsimont D, Cardoso F, Peterse H, Nuyten D, Buyse M, Van de Vijver MJ, Bergh J, Piccart M, Delorenzi M., J. Natl. Cancer Inst. 98(4), 2006
PMID: 16478745
Distinct molecular mechanisms underlying clinically relevant subtypes of breast cancer: gene expression analyses across three different platforms.
Sorlie T, Wang Y, Xiao C, Johnsen H, Naume B, Samaha RR, Borresen-Dale AL., BMC Genomics 7(), 2006
PMID: 16729877

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