RNA-seq Volcano Plot Visualizer
Interactive −log₁₀(p-adj) versus log₂ fold-change volcano plots from your own DESeq2, edgeR or limma results table. Thresholding and plotting only — no differential-expression model is run.
How it works
Differential expression results are plotted as −log₁₀(adjusted p-value) against log₂ fold change, so that both statistical confidence and effect size are visible at once. Significance and fold-change thresholds are drawn as cut-off lines, and the most extreme genes are labelled.
Frequently asked questions
How do you read a volcano plot?
The horizontal axis is log₂ fold change, so points to the right are up-regulated and to the left down-regulated; a value of 1 means a doubling. The vertical axis is −log₁₀ of the adjusted p-value, so higher points are more statistically confident. Genes of interest sit in the upper corners: large effect size and strong statistical support. Points high in the centre are confident but small effects.
Why use adjusted p-values in RNA-seq?
Testing twenty thousand genes at a 0.05 threshold would produce around a thousand false positives by chance alone. Adjustment methods such as Benjamini-Hochberg control the false discovery rate, so an adjusted p-value of 0.05 means roughly 5% of the genes called significant are expected to be false. Reporting unadjusted p-values from a genome-wide test overstates confidence dramatically.