Abstract
Proteomic clustering analysis provides a means of identifying relationships and visualizing those relationships in an extremely complex field of study with many interacting parts. With recent high-throughput studies of Src Homology 2 (SH2) domains, many and varied datasets are being amassed. A strategy for analyzing patterns between these large datasets is required to transform the information into knowledge. The methods for creating neighbor-joining phylogenetic trees, pairs scatter plots, and two-dimensional hierarchical clustering heatmaps are just a few of the diverse methods available to a proteomic researcher. This chapter examines selecting objects to be analyzed, selecting comparison functions to apply to those objects, and pseudo-code for processing data and preparing it for various types of analyses. Here I apply clustering analysis to previous collections of SH2 domains datasets to bring insight into new binding or specificity patterns between the different SH2 domains.
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Acknowledgments
The knowledge amassed to write this chapter was based on work supported by the University of Chicago Cancer Research Foundation Women’s Board and Piers Nash’s laboratory at the University of Chicago Ben May Department for Cancer Research.
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Jablonowski, K. (2017). Proteomic Clustering Analysis of SH2 Domain Datasets. In: Machida, K., Liu, B. (eds) SH2 Domains. Methods in Molecular Biology, vol 1555. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6762-9_7
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DOI: https://doi.org/10.1007/978-1-4939-6762-9_7
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