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Updated: Dec 25, 2025
Isolation of Nuclei from Flash-Frozen Liver Tissue for Single-Cell Multiomics
Published on: December 9, 2022
Efficient identification of multiple pathways: RNA-Seq analysis of livers from 56Fe ion irradiated mice
Anna M Nia1, Tianlong Chen2, Brooke L Barnette1
1Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, Texas, USA.
Background:
mRNA interaction with other mRNAs and other signaling molecules determine different biological pathways and functions. Gene co-expression network analysis methods have been widely used to identify correlation patterns between genes in various biological contexts (e.g., cancer, mouse genetics, yeast genetics). A challenge remains to identify an optimal partition of the networks where the individual modules (clusters) are neither too small to make any general inferences, nor too large to be biologically interpretable. Clustering thresholds for identification of modules are not systematically determined and depend on user-settable parameters requiring optimization. The absence of systematic threshold determination may result in suboptimal module identification and a large number of unassigned features.
Results:
In this study, we propose a new pipeline to perform gene co-expression network analysis. The proposed pipeline employs WGCNA, a software widely used to perform different aspects of gene co-expression network analysis, and Modularity Maximization algorithm, to analyze novel RNA-Seq data to understand the effects of low-dose 56Fe ion irradiation on the formation of hepatocellular carcinoma in mice. The network results, along with experimental validation, show that using WGCNA combined with Modularity Maximization, provides a more biologically interpretable network in our dataset, than that obtainable using WGCNA alone. The proposed pipeline showed better performance than the existing clustering algorithm in WGCNA, and identified a module that was biologically validated by a mitochondrial complex I assay.
Conclusions:
We present a pipeline that can reduce the problem of parameter selection that occurs with the existing algorithm in WGCNA, for applicable RNA-Seq datasets. This may assist in the future discovery of novel mRNA interactions, and elucidation of their potential downstream molecular effects.
Insights
This study introduces a new pipeline for gene co-expression network analysis, improving biological interpretability and reducing parameter selection issues in RNA-Seq data. The method enhances the discovery of mRNA interactions and their molecular effects.
Area of Science:
- Genomics
- Systems Biology
- Bioinformatics
Background:
- Gene co-expression network analysis identifies gene correlations for biological pathways.
- Current methods face challenges in determining optimal network module sizes for biological interpretability.
- User-settable parameters in existing algorithms often lead to suboptimal module identification.
Purpose of the Study:
- To develop a novel pipeline for gene co-expression network analysis using RNA-Seq data.
- To improve the biological interpretability of gene networks derived from mouse hepatocellular carcinoma models.
- To address limitations in parameter selection and module identification within existing WGCNA methods.
Main Methods:
- The study employed the Weighted Gene Co-expression Network Analysis (WGCNA) software.
- A Modularity Maximization algorithm was integrated with WGCNA for enhanced network analysis.
- The pipeline was applied to RNA-Seq data investigating the effects of 56Fe ion irradiation.
Main Results:
- The combined WGCNA and Modularity Maximization pipeline yielded more biologically interpretable networks compared to WGCNA alone.
- The proposed pipeline demonstrated superior performance over existing clustering algorithms within WGCNA.
- A key gene module identified by the pipeline was experimentally validated using a mitochondrial complex I assay.
Conclusions:
- A new pipeline effectively addresses parameter selection challenges in WGCNA for RNA-Seq datasets.
- This approach facilitates the discovery of novel mRNA interactions.
- The pipeline aids in elucidating the downstream molecular effects of identified gene networks.
