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Clustering and Network Analysis of Reverse Phase Protein Array Data

  • Adam Byron
Part of the Methods in Molecular Biology book series (MIMB, volume 1606)

Abstract

Molecular profiling of proteins and phosphoproteins using a reverse phase protein array (RPPA) platform, with a panel of target-specific antibodies, enables the parallel, quantitative proteomic analysis of many biological samples in a microarray format. Hence, RPPA analysis can generate a high volume of multidimensional data that must be effectively interrogated and interpreted. A range of computational techniques for data mining can be applied to detect and explore data structure and to form functional predictions from large datasets. Here, two approaches for the computational analysis of RPPA data are detailed: the identification of similar patterns of protein expression by hierarchical cluster analysis and the modeling of protein interactions and signaling relationships by network analysis. The protocols use freely available, cross-platform software, are easy to implement, and do not require any programming expertise. Serving as data-driven starting points for further in-depth analysis, validation, and biological experimentation, these and related bioinformatic approaches can accelerate the functional interpretation of RPPA data.

Key words

Bioinformatics Cell signaling Data analysis Hierarchical clustering Interaction networks Microarray analysis Pathway analysis Proteomics Reverse phase protein array Visualization 

Notes

Acknowledgments

A.B. is funded by Cancer Research UK (grant C157/A15703 to M. C. Frame).

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© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Cancer Research UK Edinburgh Centre, Institute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK

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