Abstract
Cluster methods are typically applied to time course gene expression data to find co-regulated genes which can finally help to reveal pathways and interactions between genes. Clustering is either carried out on the raw data or on functional data. In functional data analysis a curve is fit to each observation in order to account for time dependency. As gene expression over time is biologically a continuous process it can be represented by a continuous function. The different curve shapes found in a dataset can have important interpretations and characteristic patterns can be found by clustering the estimated regression coefficients.
In this simulation study on artificial data the well-known K-Means algorithm as well as the quality-based cluster algorithm QT-Clust are applied to both the raw data as well as functional data. The performance of the different methods is evaluated when different types of noise are added to the data. All cluster algorithms used are implemented in R.
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Acknowledgements
This work was supported by the Austrian K ind /K net Center of Biopharmaceutical Technology (ACBT).
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Scharl, T., Leisch, F. (2009). Quality-Based Clustering of Functional Data: Applications to Time Course Microarray Data. In: Fink, A., Lausen, B., Seidel, W., Ultsch, A. (eds) Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01044-6_62
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DOI: https://doi.org/10.1007/978-3-642-01044-6_62
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