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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Cluster-based network proximities for arbitrary nodal subsets.

Kenneth S Berenhaut¹, Peter S Barr², Alyssa M Kogel^3,4

¹Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, NC, 27109, USA. berenhks@wfu.edu.

Scientific Reports

|September 27, 2018

Summary

This summary is machine-generated.

This study introduces a novel unified measure for network proximity using random walks and geodesic distance. This approach enhances cluster detection and community identification across diverse scientific fields.

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Area of Science:

Network science
Data analysis
Computational social science

Background:

Network analysis frequently involves identifying clusters or communities.
Existing methods for community detection and clustering vary in complexity and applicability.
Understanding network structures is crucial in diverse scientific domains.

Purpose of the Study:

To introduce a unified measure of cluster-based proximity between network nodes.
To provide a flexible approach applicable to various network types and subsets of interest.
To enhance the dialogue on defining network clusters and communities.

Main Methods:

Utilizing random walks and geodesic distance to quantify node proximity.
Developing a unified measure applicable to specific subsets of nodes within a network.
Comparing the proposed method with existing approaches using metrics like Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI).

Main Results:

The proposed unified measure effectively quantifies cluster-based proximity.
The method demonstrates applicability to diverse datasets, including multipartite networks.
Community detection is presented as a limiting case of the broader clustering approach.
Performance comparisons show favorable results against established methods.

Conclusions:

The novel proximity measure offers a simple yet informative tool for network analysis.
This approach can aid in identifying clusters with shared attributes, relevant for targeted interventions (e.g., in health-related social networks).
The unified framework expands the conceptual understanding of network clusters and communities.