Abstract
Cellular phenotypes result from the combined effect of multiple genes, and high-throughput techniques such as DNA microarrays and deep sequencing allow monitoring this genomic complexity. The large scale of the resulting data, however, creates challenges for interpreting results, as primary analysis often yields hundreds of genes. Gene Ontology (GO), a controlled vocabulary for gene products, enables semantic analysis of such gene sets. GO can be used to define semantic similarity between genes, which enables semantic clustering to reduce the complexity of a result set. Here, we describe how to compute semantic similarities and perform GO-based gene clustering using csbl.go, an R package for GO semantic similarity. We demonstrate the approach with expression profiles from breast cancer.
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I thank Tiia Pelkonen for proofreading.
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Ovaska, K. (2015). Using Semantic Similarities and csbl.go for Analyzing Microarray Data. 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_241
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DOI: https://doi.org/10.1007/7651_2015_241
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Publisher Name: Humana Press, New York, NY
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Online ISBN: 978-1-4939-3173-6
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