Create publication-ready volcano plots from differential-expression results.
Upload fold-change and adjusted p-value data, describe thresholds and labels, then generate a clean AI-made volcano plot with manuscript-ready PNG output.
Upload data and describe the AI chart you need.
Costs 5 credits. Uses your uploaded data and prompt to generate a publication-ready PNG.
Preview
Aspect ratio: 16:9
These are sample previews. Upload your dataset to generate your own volcano plot.
Use these examples as visual directions for RNA-seq, proteomics, biomarker screens, threshold views, and manuscript-ready volcano plots.
Prompt
Based on the uploaded rna_seq_differential_expression.csv file, create a publication-ready volcano plot for RNA-seq differential expression. Use log2 fold change on the x-axis, adjusted significance on the y-axis, dashed threshold guides, red upregulated hits, blue downregulated hits, light gray nonsignificant points, and concise labels for the strongest genes.
Prompt
Based on the uploaded proteomics_abundance_response.xlsx file, create a volcano plot showing protein abundance changes after treatment. Plot log2 fold change against adjusted p-value significance, highlight increased and decreased proteins with distinct colors, label only high-confidence proteins, and keep the white-background scientific style clean.
Prompt
Based on the uploaded treatment_control_rnaseq.tsv file, create a treatment-versus-control volcano plot. Use symmetric x-axis limits around zero, color significant positive and negative fold changes separately, keep dense nonsignificant genes subtle, add compact threshold annotations, and label selected genes from the uploaded feature column.
Prompt
Based on the uploaded biomarker_screen_results.csv file, create a volcano plot for biomarker screening results. Emphasize large-effect candidates with high adjusted significance, label the top biomarkers, use a clean legend for upregulated, downregulated, and nonsignificant groups, and avoid labeling crowded background points.
Prompt
Based on the uploaded multi_threshold_screen.csv file, create a volcano plot that clearly shows fold-change and FDR threshold boundaries. Add vertical and horizontal guide lines, use restrained colors for significant groups, keep nonsignificant points pale, and label only points that pass both the effect-size and significance thresholds.
Prompt
Based on the uploaded publication_volcano_hits.xlsx file, create a clean monochrome volcano plot suitable for a journal figure. Use black and gray points with a limited blue accent for selected hits, clear threshold lines, readable gene labels, balanced margins, and no decorative elements.
Add CSV, TSV, TXT, or Excel results with a feature label, log fold-change column, and adjusted p-value or transformed significance column.
Tell SciFigure AI the thresholds, labels, colors, significance categories, and figure title you need.
Create a clean AI volcano plot, verify labels and thresholds, then download PNG for papers and slides.
It turns differential-expression or high-throughput screening results into a clean volcano plot that combines effect size and statistical evidence in one figure. The result is designed for manuscripts, lab reports, posters, and research presentations.
Use a feature label column, a numeric effect-size column such as log2 fold change, and a numeric significance column such as adjusted p-value, FDR, q-value, or -log10 adjusted p-value.
Yes. Map the adjusted p-value column and describe the intended y-axis. The generator can use the mapped column without double-transforming already transformed values.
Yes. Include a feature label column and describe which hits should be labeled, such as top FDR hits, named genes, or pathway members.
Yes. Describe fold-change and significance thresholds, then specify the colors for upregulated, downregulated, and nonsignificant points.
Yes, but verify the uploaded values, multiple-testing method, thresholds, labels, and caption before submission.
Generate volcano plots, heatmaps, scatter plots, and other research-ready visualizations with SciFigure AI.
