Efficient identification of multiple pathways: RA-Seq analysis of livers from ⁵⁶Fe ion irradiated mice

Background: mRA interaction with other mRAs 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 WGCA, a software widely used to perform different aspects of gene co-expression network analysis, and Modularity Maximization algorithm, to analyze novel RA-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 WGCA combined with Modularity Maximization, provides a more biologically interpretable network in our dataset, than that obtainable using WGCA alone. The proposed pipeline showed better performance than the existing clustering algorithm in WGCA, 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 WGCA, for applicable RA-Seq datasets. This may assist in the future discovery of novel mRA interactions, and elucidation of their potential downstream molecular effects.