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Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Updated: Mar 2, 2026

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

Published on: January 22, 2013

Clustering and Network Analysis of Reverse Phase Protein Array Data.

¹Cancer Research UK Edinburgh Centre, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XR, UK. adam.byron@igmm.ed.ac.uk.

Methods in Molecular Biology (Clifton, N.J.)

|May 15, 2017

Summary

This summary is machine-generated.

Computational analysis of reverse phase protein array (RPPA) data using hierarchical clustering and network analysis can reveal protein expression patterns and interactions. These bioinformatic methods accelerate the interpretation of complex proteomic datasets for biological research.

Keywords:

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

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Last Updated: Mar 2, 2026

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

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Published on: August 21, 2019

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Reverse phase protein array (RPPA) enables quantitative proteomic analysis of numerous samples.
RPPA generates high-volume, multidimensional data requiring effective computational interpretation.
Data mining techniques are crucial for exploring RPPA data structure and predicting function.

Purpose of the Study:

To detail computational approaches for analyzing RPPA data.
To demonstrate the use of hierarchical cluster analysis and network analysis for RPPA data interpretation.
To provide accessible protocols for researchers without programming expertise.

Main Methods:

Hierarchical cluster analysis for identifying similar protein expression patterns.
Network analysis for modeling protein interactions and signaling relationships.
Utilizing freely available, cross-platform software for ease of implementation.

Main Results:

Successful application of cluster and network analyses to RPPA data.
Identification of protein expression patterns and signaling networks.
Demonstration of data-driven starting points for further biological investigation.

Conclusions:

Computational analysis significantly enhances the interpretation of RPPA data.
Bioinformatic approaches like clustering and network analysis accelerate functional insights.
These methods facilitate further validation and experimental design in proteomics research.