Abstract
Recently, a great effort in microarray data analysis has been directed towards the study of the so-called gene sets. A gene set is defined by genes that are, somehow, functionally related. For example, genes appearing in a known biological pathway naturally define a gene set. Gene sets are usually identified from a priori biological knowledge. Nowadays, many bioinformatics resources store such kind of knowledge (see, for example, the Kyoto Encyclopedia of Genes and Genomes, among others). In this paper we exploit a multivariate approach, based on graphical models, to deal with gene sets defined by pathways. Given a sample of microarray data corresponding to two experimental conditions and a pathway linking some of the genes, we investigate whether the strength of the relations induced by the functional links change among the two experimental conditions.
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Massa, M.S., Chiogna, M., Romualdi, C. (2010). A graphical models approach for comparing gene sets. In: Mantovan, P., Secchi, P. (eds) Complex Data Modeling and Computationally Intensive Statistical Methods. Contributions to Statistics. Springer, Milano. https://doi.org/10.1007/978-88-470-1386-5_9
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DOI: https://doi.org/10.1007/978-88-470-1386-5_9
Publisher Name: Springer, Milano
Print ISBN: 978-88-470-1385-8
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