2.2.2. Normalization of Read Counts, Differentially Expression Analysis (DEA) and Network Construction

In order to normalize and perform differential expression analysis on counts, the standard Bioconductor RNA-seq workflow (DESeq2) was used to detect differentially expressed genes (DEGs). The distribution of expression values across all samples (normal and treatment) before and after normalization was applied to ensure that expression values were similar across normalized counts. The PPI network was constructed using STRING (*p*-value: 1.0 × <sup>10</sup>−164), which resulted in 2736 interactions between 180 nodes based on a confidence score of 0.007. In order to detect the key parameters, the interaction pairs of the network obtained from STRING were visualized by Cytoscape (Version 3.6) with a cut-off value for BC > 0 and K > 8. After analyzing PPI network modules with MCODE, generally, 10 modules obtained. Three significant modules were identified with an MCODE score ≥ 3 and nodes ≥ 3. In order to conduct a gene-pathway annotated network, 300 upregulated (padj < 0.01, log2 FC > 2) and downregulated (padj < 0.01, log2 FC < −2) genes were mapped to 117 KEGG pathways. Then, an annotated network was constructed for significant KEGG pathways by using Cytoscape (Version 3.6). Gene ontology (GO) was conducted using the enrichR/Bioconductor package to clarify which biological categories (CC, MF, BP) the DEGs are enriched.
