Volcano Plot for Differential Expression

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About This MicroSim

This MicroSim displays a volcano plot — the standard visualization for differential gene expression analysis. It plots 100 simulated genes with their log2 fold change on the x-axis and -log10 adjusted p-value on the y-axis, creating the characteristic volcano shape. Students can adjust significance thresholds and hover over individual genes to explore the data.

What Is a Volcano Plot?

A volcano plot combines two key metrics from a differential expression experiment:

Log2 fold change (x-axis) — How much a gene's expression changed between two conditions (e.g., tumor vs. normal). Positive values indicate upregulation; negative values indicate downregulation.
-log10 p-value (y-axis) — Statistical significance of the change. Higher values (further from the x-axis) indicate more statistically significant differences.

Genes in the upper-left and upper-right corners are both statistically significant and biologically meaningful — these are the most interesting candidates for further study.

Visual Encoding

Red points — Significantly upregulated genes (above p-value threshold and right of fold-change threshold)
Blue points — Significantly downregulated genes (above p-value threshold and left of negative fold-change threshold)
Gray points — Genes that do not meet both significance criteria
Vertical dashed lines — Fold-change thresholds
Horizontal dashed line — P-value significance threshold
Gene labels appear on hover, showing the gene name, fold change, and p-value

Simulated Data

The 100 genes are drawn from well-known cancer-related genes (TP53, BRCA1, MYC, EGFR, etc.) with simulated expression changes. Approximately 15% are strongly upregulated, 15% strongly downregulated, 20% are borderline, and 50% show no significant change — reflecting typical real-world distributions.

How to Use

Fold-change threshold slider — Adjust the minimum absolute log2 fold change required for significance (vertical dashed lines move)
P-value threshold slider — Adjust the -log10 p-value cutoff (horizontal dashed line moves)
Hover over any gene point to see its name, fold change value, and p-value
Regenerate button — Create a new set of 100 simulated genes with different random expression values

Suggested Experiments

Start with default thresholds and count the red (up) and blue (down) significant genes
Increase the fold-change threshold to 2.0 — watch how many genes lose significance. These were statistically significant but had small effect sizes.
Increase the p-value threshold to 3.0 (-log10 scale, meaning p < 0.001) — now only the most statistically robust genes remain
Click Regenerate several times and notice how the distribution varies — this illustrates why statistical thresholds matter

Iframe Embed Code

You can add this MicroSim to any web page by adding this to your HTML:

<iframe src="https://dmccreary.github.io/bioinformatics/sims/volcano-plot/main.html"
        height="542"
        width="100%"
        scrolling="no"></iframe>

Lesson Plan

Grade Level

College introductory bioinformatics

Duration

15-20 minutes

Prerequisites

Understanding of gene expression and how it is measured (RNA-seq)
Basic statistics: p-values and what statistical significance means
Concept of fold change as a ratio of expression levels between conditions

Activities

Exploration (5 min): With default thresholds, hover over genes in each region (red, blue, gray). For 3 genes in each category, record the gene name, fold change, and p-value. What distinguishes significant from non-significant genes?
Threshold Tuning (5 min): Set a very permissive threshold (FC > 0.5, p < 0.1) and count significant genes. Then set a very strict threshold (FC > 2.0, p < 0.001). How does threshold choice affect the number of candidate genes? Discuss the trade-off between sensitivity and specificity.
Biological Interpretation (5 min): Regenerate the data and find the gene with the highest fold change. Is it also the most statistically significant? Find the gene with the highest -log10 p-value. Is it also the most biologically changed? Discuss why both metrics are needed.
Assessment (5 min): Answer the reflection questions below.

Assessment

Why does a volcano plot use log2 fold change rather than raw fold change on the x-axis?
A gene has a log2 fold change of 3.0 but a p-value of 0.08. Would you consider this gene differentially expressed? Why or why not?
Why do volcano plots typically show a "volcano" shape — few points in the upper corners and many near the origin?
In a drug treatment experiment, you see 200 significantly upregulated genes and 5 significantly downregulated genes. What might this asymmetry suggest about the drug's mechanism?