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Structural Equation Modeling of In silico Perturbations
Jianying Li1,2,3, Pierre R Bushel3,4, Lin Lin5,6
1Integrative Bioinformatics, Epigenetics and Stem Cell Biology Laboratory, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, United States.
Frontiers in Genetics
|December 13, 2021
Summary
Scientists can now infer gene activity and interactions using the SEMIPs R Shiny application. This tool models gene networks computationally, aiding human biological system research and clinical relevance.
Area of Science:
- Genomics
- Systems Biology
- Bioinformatics
Background:
- Gene expression regulation involves complex interactions between multiple regulators.
- Genome-wide expression data can model human biological systems for disease research.
- Inferring hidden gene interactions is crucial for understanding biological pathways.
Purpose of the Study:
- To develop an R Shiny application, SEMIPs, for inferring gene interactions and activities.
- To enable computational modeling of gene networks using structural equation modeling (SEM).
- To provide a tool for analyzing gene expression data to understand biological processes with clinical relevance.
Main Methods:
- Developed the "Structural Equation Modeling of In silico Perturbations (SEMIPs)" R Shiny application.
- Implemented a 3-node SEM model to analyze interactions between regulators and downstream targets.
- Utilized gene expression data to compute a t-statistic (T-score) as a surrogate for gene activity.
Main Results:
- SEMIPs computes T-scores from gene expression data to estimate regulator activity.
- The application facilitates correlational studies and model fitting for multiple variables.
- A case study demonstrated inferring GATA2 activity within the PGR-GATA2-SOX17 network.
Conclusions:
- SEMIPs enables *in silico* investigation of gene interactions.
- The tool aids in understanding complex gene networks relevant to human health.
- Computational modeling of gene activity is feasible using downstream gene expression data.
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