A volcano plot displays statistical significance (-log10 p-value) on the y-axis versus effect size (log2 fold change) on the x-axis. Points are typically color-coded to highlight features that are both statistically significant and have large effect sizes. This visualization is essential for quickly identifying the most important changes in differential expression, proteomics, and genomics studies.
""" anyplot.ai
volcano-basic: Volcano Plot for Statistical Significance
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-14
"""
import os
import time
from pathlib import Path
import numpy as np
from bokeh.io import output_file, save
from bokeh.models import ColumnDataSource, HoverTool, Span
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
# Okabe-Ito palette
COLOR_UP = "#C475FD" # vermillion
COLOR_DOWN = "#4467A3" # blue
COLOR_NS = "#AAAAAA" # neutral gray
# Data - Simulated differential expression results
np.random.seed(42)
n_genes = 2000
# Generate log2 fold changes (effect sizes)
log2_fc = np.random.normal(0, 1.5, n_genes)
# Generate p-values: most genes non-significant, some highly significant
# Use inverse relationship with fold change magnitude for realism
base_pvals = np.random.uniform(0.001, 1, n_genes)
fold_effect = np.abs(log2_fc) / np.max(np.abs(log2_fc))
pvals = base_pvals * (1 - 0.7 * fold_effect) + 0.01 * np.random.random(n_genes)
pvals = np.clip(pvals, 1e-50, 1)
neg_log10_pval = -np.log10(pvals)
# Significance thresholds
pval_threshold = -np.log10(0.05) # ~1.3
fc_threshold = 1.0 # log2(2) = 1
# Classify points by significance
significant_up = (neg_log10_pval > pval_threshold) & (log2_fc > fc_threshold)
significant_down = (neg_log10_pval > pval_threshold) & (log2_fc < -fc_threshold)
# Create separate data sources for each category (enables proper legend)
source_up = ColumnDataSource(
data={
"x": log2_fc[significant_up],
"y": neg_log10_pval[significant_up],
"description": [
f"Up-regulated: FC={x:.2f}, p={10 ** (-y):.2e}"
for x, y in zip(log2_fc[significant_up], neg_log10_pval[significant_up], strict=True)
],
}
)
source_down = ColumnDataSource(
data={
"x": log2_fc[significant_down],
"y": neg_log10_pval[significant_down],
"description": [
f"Down-regulated: FC={x:.2f}, p={10 ** (-y):.2e}"
for x, y in zip(log2_fc[significant_down], neg_log10_pval[significant_down], strict=True)
],
}
)
source_ns = ColumnDataSource(
data={
"x": log2_fc[~(significant_up | significant_down)],
"y": neg_log10_pval[~(significant_up | significant_down)],
"description": [
f"Not significant: FC={x:.2f}, p={10 ** (-y):.2e}"
for x, y in zip(
log2_fc[~(significant_up | significant_down)], neg_log10_pval[~(significant_up | significant_down)], strict=True
)
],
}
)
# Create figure
p = figure(
width=4800,
height=2700,
title="volcano-basic · bokeh · anyplot.ai",
x_axis_label="Log₂ Fold Change",
y_axis_label="-Log₁₀ (P-value)",
)
# Hover tool
hover = HoverTool(tooltips=[("", "@description")])
p.add_tools(hover)
# Plot points by category (non-significant first, then significant on top)
p.scatter(x="x", y="y", source=source_ns, color=COLOR_NS, size=18, alpha=0.5, legend_label="Not significant")
p.scatter(x="x", y="y", source=source_down, color=COLOR_DOWN, size=25, alpha=0.7, legend_label="Down-regulated")
p.scatter(x="x", y="y", source=source_up, color=COLOR_UP, size=25, alpha=0.7, legend_label="Up-regulated")
# Add threshold lines
hline = Span(
location=pval_threshold, dimension="width", line_color=INK_SOFT, line_dash="dashed", line_width=3, line_alpha=0.5
)
p.add_layout(hline)
vline_pos = Span(
location=fc_threshold, dimension="height", line_color=INK_SOFT, line_dash="dashed", line_width=3, line_alpha=0.5
)
p.add_layout(vline_pos)
vline_neg = Span(
location=-fc_threshold, dimension="height", line_color=INK_SOFT, line_dash="dashed", line_width=3, line_alpha=0.5
)
p.add_layout(vline_neg)
# Styling - scaled for 4800x2700 canvas
p.title.text_font_size = "28pt"
p.title.text_color = INK
p.xaxis.axis_label_text_font_size = "22pt"
p.yaxis.axis_label_text_font_size = "22pt"
p.xaxis.axis_label_text_color = INK
p.yaxis.axis_label_text_color = INK
p.xaxis.major_label_text_font_size = "18pt"
p.yaxis.major_label_text_font_size = "18pt"
p.xaxis.major_label_text_color = INK_SOFT
p.yaxis.major_label_text_color = INK_SOFT
p.xaxis.axis_line_color = INK_SOFT
p.yaxis.axis_line_color = INK_SOFT
p.xaxis.major_tick_line_color = INK_SOFT
p.yaxis.major_tick_line_color = INK_SOFT
# Grid styling
p.xgrid.grid_line_color = INK
p.ygrid.grid_line_color = INK
p.xgrid.grid_line_alpha = 0.1
p.ygrid.grid_line_alpha = 0.1
# Background
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = INK_SOFT
# Legend styling
p.legend.location = "top_right"
p.legend.label_text_font_size = "18pt"
p.legend.label_text_color = INK_SOFT
p.legend.glyph_height = 40
p.legend.glyph_width = 40
p.legend.spacing = 15
p.legend.padding = 20
p.legend.background_fill_color = ELEVATED_BG
p.legend.border_line_color = INK_SOFT
# Save HTML
output_file(f"plot-{THEME}.html")
save(p)
# Screenshot with headless Chrome
W, H = 4800, 2700
opts = Options()
for arg in (
"--headless=new",
"--no-sandbox",
"--disable-dev-shm-usage",
"--disable-gpu",
f"--window-size={W},{H}",
"--hide-scrollbars",
):
opts.add_argument(arg)
driver = webdriver.Chrome(options=opts)
driver.set_window_size(W, H)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
time.sleep(3)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
Part of Volcano Plot for Statistical Significance on anyplot.ai.