Volcano plots and heatmaps are common visual summaries for RNA-seq differential expression results.
RNA-seq analysis can produce large gene tables with fold changes, p-values, adjusted p-values, and expression values. Visualization helps researchers identify patterns that are difficult to see from tables alone.
A volcano plot usually shows log2 fold change on the x-axis and statistical significance on the y-axis. Genes far to the left or right have stronger expression changes, while genes higher on the plot have stronger statistical support.
Genes in the upper right are often interpreted as significantly upregulated, while genes in the upper left are often interpreted as significantly downregulated. However, thresholds for fold change and adjusted p-value should be chosen carefully and should match the biological question.
Heatmaps display expression patterns across genes and samples. They can show whether selected genes cluster by condition, whether replicates behave consistently, and whether a subset of genes has strong condition-specific expression.
Use plots together with statistical results, metadata review, QC information, and biological knowledge. Important genes should be validated using independent methods or datasets when possible.
This guide is provided for research and educational purposes. RNA-seq results should be interpreted with appropriate experimental design, quality control, statistical review, and biological validation.