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Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

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Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis.

Wei-Feng Guo^1,2, Xiangtian Yu³, Qian-Qian Shi⁴

¹School of Electrical Engineering, Zhengzhou University, Zhengzhou, China.

Plos Computational Biology

|May 6, 2021

Summary

This study benchmarks 16 sample-specific network control (SSC) workflows. Cell-Specific Network construction (CSN) and Single-Sample Network (SSN) methods combined with undirected network control are recommended for biological network analysis.

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

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Area of Science:

Systems biology
Network science
Bioinformatics

Background:

Sample-specific network construction and control methods are crucial for identifying driver nodes in biological systems.
Existing methods lack comprehensive performance evaluation, hindering optimal application in Sample-Specific network Control (SSC) analysis.

Purpose of the Study:

To conduct a comprehensive performance assessment of 16 state-of-the-art SSC analysis workflows.
To identify optimal combinations of network construction and control methods for biological network analysis.
To provide benchmarked workflows and recommendations for SSC analysis.

Main Methods:

Evaluated 16 SSC analysis workflows, combining 4 sample-specific network reconstruction methods (including Cell-Specific Network construction (CSN) and Single-Sample Network (SSN)) with 4 structural control methods.
Utilized simulation evaluation on biological networks, personalized driver gene prioritization on TCGA cancer datasets, and cell marker identification on single-cell RNA-seq data.

Main Results:

The performance of network control methods is highly dependent on the upstream sample-specific network construction method.
Cell-Specific Network construction (CSN) and Single-Sample Network (SSN) methods are identified as preferred network construction approaches.
Undirected network-based control methods demonstrated higher effectiveness compared to directed methods post-network construction.

Conclusions:

Recommends CSN and SSN methods coupled with undirected network control for robust SSC analysis.
Provides a benchmarked evaluation pipeline and data freely available for the research community.
Highlights the importance of selecting appropriate upstream methods for effective biological network control.