Abstract
Gene expression data (microarrays and RNA-sequencing data) as well as other kinds of genomic data can be extracted from publicly available genomic data. Here, we explain how to apply multivariate cluster and classification methods on gene expression data. These methods have become very popular and are implemented in freely available software in order to predict the participation of gene products in a specific functional category of interest. Taking into account the availability of data and of these methods, every biological study should apply them in order to obtain knowledge on the organism studied and functional category of interest. A special emphasis is made on the nonlinear kernel classification methods.
An erratum of the original chapter can be found under DOI 10.1007/7651_2015_256
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Kleine, L.L., Montaño, R., Torres-Avilés, F. (2015). Classification and Clustering on Microarray Data for Gene Functional Prediction Using R. In: Guzzi, P. (eds) Microarray Data Analysis. Methods in Molecular Biology, vol 1375. Humana Press, New York, NY. https://doi.org/10.1007/7651_2015_240
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DOI: https://doi.org/10.1007/7651_2015_240
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