Abstract
Thanks for the dramatic reduction of the costs of high-throughput techniques in modern biotechnology, searching for differentially expressed genes is already a common procedure in identifying biomarkers or signatures of phenotypic states such as diseases or compound treatments. However, in most of the cases, especially in complex diseases, even given a list of biomarkers, the underlying biological mechanisms are still obscure to us. In other words, rather than knowing what genes are involved, we are more interested in discovering the common, collective roles of all these genes. Based on the assumption that genes involved in the same biological processes, functions, or localizations present correlated behaviors in terms of expression levels, signal intensities, allele occurrences, and so on, we can therefore apply statistical tests to find perturbed pathways. Gene Set/Pathway enrichment analysis is one of such techniques; a step-by-step instruction is described in this chapter.
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Acknowledgments
I would like to thank everyone who supported me during the production of this chapter. Special thanks to the editor and proofreader. A final thank you is due to Charles DeLisi, who never gets tired of my stupidity.
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Hung, JH. (2013). Gene Set/Pathway Enrichment Analysis. In: Mamitsuka, H., DeLisi, C., Kanehisa, M. (eds) Data Mining for Systems Biology. Methods in Molecular Biology, vol 939. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-107-3_13
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DOI: https://doi.org/10.1007/978-1-62703-107-3_13
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