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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

Global spectral clustering in dynamic networks.

Fuchen Liu¹, David Choi², Lu Xie¹

¹Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213.

Proceedings of the National Academy of Sciences of the United States of America

|January 18, 2018

Summary

This summary is machine-generated.

Persistent communities by eigenvector smoothing (PisCES) enhances community detection in uncertain dynamic networks. This novel method improves inference by integrating longitudinal network data, outperforming existing approaches in low signal-to-noise conditions.

Keywords:

community detection dynamic networks gene expression networks

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Area of Science:

Network science
Computational biology
Developmental neuroscience

Background:

Community detection in networks with uncertain structures is difficult.
Dynamic networks offer temporal information but introduce complexity.
Existing methods struggle with low signal-to-noise ratios and small sample sizes.

Purpose of the Study:

To introduce Persistent Communities by Eigenvector Smoothing (PisCES), a global community detection method for dynamic networks.
To leverage longitudinal network data for robust inference in each time period.
To provide data-driven parameter selection for the PisCES method.

Main Methods:

PisCES integrates information across a series of networks longitudinally.
The method is derived from evolutionary spectral clustering and degree correction.
Data-driven solutions for tuning parameter selection are incorporated.

Main Results:

PisCES outperforms competing methods in simulations, especially under low signal-to-noise conditions.
Application to rhesus monkey brain gene expression data reveals dynamic community structures.
The method successfully identifies persistent, merging, and diverging gene communities over developmental time.

Conclusions:

PisCES offers a powerful approach for dynamic community detection in challenging network data.
The method provides insights into the temporal development of biological systems, such as brain gene communities.
PisCES addresses limitations of small sample sizes in longitudinal studies.