plot-volcano

To create a publication-quality volcano plot from RNA-seq differential expression results, provide a tabular DE results file with log2FC and p-value columns. The tool combines fold-change and statistical significance into a single visualization with smart label placement and quadrant counts.

Yes, you can use volcano plots with DESeq2, edgeR, or limma output tables. The tool auto-detects common DE columns like gene or symbol names and applies flexible column mappings to minimize preprocessing across these standard RNA-seq workflows.

Available labeling strategies for highlighting genes on a volcano plot include labeling top features by score, highlighting specific gene sets, or combining both approaches. The tool uses adjustText for smart label placement to prevent overlapping text.

The volcano plot generator supports custom fold-change and p-value cutoffs, including both asymmetric and symmetric fold-change thresholds. You can also apply continuous coloring by an expression metric or use categorical coloring with custom colors.

The volcano plot tool exports PNG as the default output format and optionally SVG for scalable vector graphics. It also outputs an annotated result table containing the processed differential expression data for downstream analysis.

The tool handles large feature sets in differential expression data by integrating with common RNA-seq DE workflows and applying efficient matplotlib-based rendering. It processes large numbers of features while maintaining publication-ready styling and smart label placement.