WDNE: an integrative graphical model for inferring differential networks from multi-platform gene expression data
Le Ou-Yang1, Dehan Cai2, Xiao-Fei Zhang3
1Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Media Security, and Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ), College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China.
Briefings in Bioinformatics
|May 11, 2021
Summary
This study introduces a new Weighted Differential Network Estimation (WDNE) model to analyze gene expression data, effectively handling missing values and identifying gene network changes crucial for understanding disease mechanisms.
Area of Science:
- Computational Biology
- Genomics
- Systems Biology
Background:
- Biological processes involve complex gene dependency networks.
- High-throughput gene expression data (microarray, RNA sequencing) enable network inference.
- Existing methods struggle with missing data common in single-cell RNA sequencing.
Purpose of the Study:
- To develop a novel method for differential gene network analysis that accommodates missing data.
- To incorporate changes in gene expression levels to identify gene network rewiring.
- To provide a computational tool for analyzing multi-platform gene expression data.
Main Methods:
- Proposed a Weighted Differential Network Estimation (WDNE) model.
- Designed to handle multi-platform gene expression data with missing values.
- Incorporates gene expression level changes for network rewiring identification.
Main Results:
- WDNE outperforms existing differential network estimation methods in simulations.
- Applied WDNE to analyze drug resistance in ovarian tumors, cell differentiation, and breast tumor heterogeneity.
- Identified key hub genes providing insights into underlying biological mechanisms.
Conclusions:
- WDNE is a robust method for differential gene network analysis, especially with missing data.
- The model offers valuable insights into complex biological processes like disease development and cell differentiation.
- A user-friendly Matlab toolbox facilitates the application and visualization of WDNE.
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