bio-data-visualization-volcano-customization

What problem does it solve?

Creating volcano plots from differential expression results can be time-consuming and error-prone, especially when you need consistent thresholds, clear gene labels, and publication-ready visuals.

Core Features & Use Cases

• Flexible visualization backends: ggplot2, EnhancedVolcano (R), and matplotlib (Python) to match your workflow.
• Threshold customization: adjust log2 fold-change and p-value cutoffs to capture key genes.
• Gene labeling and highlighting: add non-overlapping labels and spotlight genes of interest for emphasis.
• Real-world use case: produce a polished volcano plot for a DE analysis manuscript, including labeled top genes and color-coded significance.

Quick Start

Input a differential expression results table with columns for gene, log2FoldChange, and p-values, then choose a backend to generate a publication-ready volcano plot.