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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

Single-cell Co-expression Subnetwork Analysis.

Thomas E Bartlett¹, Sören Müller², Aaron Diaz²

¹Department of Statistical Science, University College, London, UK. thomas.bartlett.10@ucl.ac.uk.

Scientific Reports

|November 10, 2017

Summary

This summary is machine-generated.

This study adapts genomic network analysis for single-cell transcriptomic data, overcoming challenges like noise. Using mixture models, it infers gene relationships, enabling functional subnetwork detection.

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Area of Science:

Genomic science
Computational biology
Systems biology

Background:

Single-cell transcriptomic data is increasingly popular in genomic science.
Network models are crucial for understanding gene functions and biological processes.
Challenges like zero inflation and technical noise in single-cell data necessitate adapted analytical methods.

Purpose of the Study:

To assess established genomic network analysis methods for single-cell data suitability.
To adapt existing methods to address the unique challenges of single-cell transcriptomic data.
To infer binary gene-gene relationships from continuous correlations for network analysis.

Main Methods:

Assessment of established genomic network analysis techniques.
Adaptation of methods for the single-cell transcriptomic data context.
Application of mixture models to infer binary relationships from gene-gene correlations.

Main Results:

Established genomic network analysis methods can be effectively adapted for single-cell data.
Mixture models successfully infer binary relationships from gene-gene correlations.
Subnetwork analysis on inferred binary relationships accurately detects functional modules.

Conclusions:

Genomic network analysis can be successfully applied to single-cell transcriptomic data.
The proposed adaptation using mixture models and subnetwork analysis is effective.
This approach facilitates the discovery of functional gene modules in single-cell networks.