Raw expression data from the GSE30165 dataset were normalized by applying background correction, quantile normalization, and log₂ transformation to reduce technical variation. Probe identifiers from the GPL7294 platform were mapped to official Rattus norvegicus gene symbols using the GPL annotation table in combination with the org.Rn.eg.db package. Where multiple probes mapped to the same gene, expression values were averaged. Probes that could not be mapped were excluded from further analysis.
Peripheral Nerve Regeneration Genes
Data Analysis
To evaluate data quality, principal component analysis (PCA) was used to confirm that samples clustered by tissue and time point. Low-variance probes were removed, and the top 3,000 most variable genes were retained to focus on biologically informative expression patterns.
Differential expression analysis was performed using the limma package, contrasting injured versus control samples at each time point. Genes were standardized by z-score transformation to enable comparison across time. Temporal expression patterns were then grouped into six clusters using k-means clustering. Cluster trajectories were visualized with line plots and heatmaps to highlight distinct dynamic trends over the course of injury and recovery.
Functional annotation was carried out with the clusterProfiler package. For each gene cluster, Gene Ontology (GO) enrichment was performed for Biological Process, Cellular Component, and Molecular Function categories. Terms were considered significant at a false discovery rate (FDR) below 0.05. In parallel, KEGG pathway enrichment was conducted for Rattus norvegicus with a significance cutoff of q < 0.10. Enrichment results were visualized with dotplots and exported alongside the corresponding tables in CSV format.
All analyses were conducted separately for dorsal root ganglia (DRG) and sciatic nerve (SN) samples to capture tissue-specific responses. Sample metadata were parsed to classify tissue identity and to encode time points numerically (in hours), allowing for alignment of temporal expression trajectories.