Inter-sample correlation heatmap visually displays the similarity of expression profiles or data patterns between different samples via color intensity, helping assess data reproducibility and sample grouping consistency.

Principal component analysis (PCA) result visually reduces high-dimensional data complexity, revealing overall patterns, sample clustering, and potential outliers to assess data quality and biological variability.

The Co-expression Venn diagram intuitively identifies common and unique coexpressed gene sets across multiple experimental groups, facilitating the discovery of shared or specific coexpression patterns.

The volcano plot visualizes fold change and statistical significance (p-value) of DEGs, enabling rapid identification of significantly up- or downregulated genes.

This heatmap clusters differentially expressed genes by their expression patterns, visually highlighting similarities/differences in gene expression across samples to facilitate the identification of functionally related gene groups.

This scatter plot visualizes enrichment analysis results by mapping enriched terms (e.g., pathways/GO terms) against metrics like enrichment score and significance, enabling intuitive identification of key functional categories associated with target genes.

By constructing protein interaction networks, this analysis uncovers core functional modules, hub proteins, and molecular mechanisms underlying biological processes or diseases.