The data from GSE30165 includes multiple time points from dorsal root ganglia (DRG) and sciatic nerve (SN) tissues. It was necessary to classify these time points in order to determine which genes were expressed or no longer expressed over the course of the study. After gathering and normalizing the data, statistical analyses were conducted to identify how genes were expressed across different time points and tissue types. Pairwise comparisons between the experimental conditions (injured vs. control) were performed using limma, an R package. The empirical Bayes method was applied to moderate standard errors, improving statistical power in the presence of small sample sizes.
Peripheral Nerve Regeneration Genes
Testing
To ensure significance of the genes they were filtered using adjusted p-values which was done using Benjamini–Hochberg false discovery rate, FDR < 0.05. The genes were also filtered using log₂ fold change thresholds (|log₂FC| ≥ 1.0).
Data quality was assessed and evaluated for reproducibility. Principal Component Analysis (PCA) was performed to examine clustering of biological replicates and separation between experimental groups. Hierarchical clustering was applied to the DEGs to assess similarities between samples and time points. Volcano plots and MA plots were used to visualize the distribution of DEGs by fold change and statistical significance.
Temporal expression trends of the DEGs were examined to identify genes showing upregulation or downregulation during the injury recovery period. These patterns were subsequently used as input for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, allowing for functional interpretation of the observed gene expression changes.