Summary
In this chapter we present the decomposition approach to the analysis of large gene expression profile data sets. We address the problem of analysis of transient time-course data of expression profiles. We accept the assumption that co-expression of genes can be related to their belonging to the same Gaussian component. We assume that parameters of Gaussian components, means and variances, can differ between time instants. However, the gene composition of components is unchanged between time instants. For such problem formulations we derive the appropriate version of expectation-maximization algorithm recursions for the estimation of model parameters.We apply the derived method to the data on gene expression profiles of human K562 erythroleukemic cells and we discuss the obtained gene clustering.
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Polańska, J., Widĺak, P., Rzeszowska-Wolny, J., Kimmel, M., Polański, A. (2007). Gaussian Mixture Decomposition of Time-Course DNA Microarray Data. In: Deutsch, A., Brusch, L., Byrne, H., Vries, G.d., Herzel, H. (eds) Mathematical Modeling of Biological Systems, Volume I. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4558-8_31
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DOI: https://doi.org/10.1007/978-0-8176-4558-8_31
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