Abstract
An increasing number of methodologies are available for finding functional genomic clusters in RNA expression data. In this chapter, we describe a technique, termed relevance networks, that computes comprehensive pairwise measures of similarity for all genes in such a dataset. Associations with high positive or negative measures are saved and displayed in a graph-network-type diagram. Advantages of this method over others include: (1) negative associations (e.g., those from tumor suppressing genes) are shown: (2) disparate data types can be included (i.e., clinical, expression, and phenotypic); and (3) multiple connections are allowed (e.g., a transcription factor may be responsible for regulating the expression of multiple other genes). Java-based software is available for academic use to construct relevance networks, and operation of the software is also explained in this chapter.
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Butte, A.J., Kohane, I.S. (2003). Relevance Networks: A First Step Toward Finding Genetic Regulatory Networks Within Microarray Data. In: Parmigiani, G., Garrett, E.S., Irizarry, R.A., Zeger, S.L. (eds) The Analysis of Gene Expression Data. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/0-387-21679-0_19
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DOI: https://doi.org/10.1007/0-387-21679-0_19
Publisher Name: Springer, New York, NY
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