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feat(plotly): implement volcano-basic (#2942)
## Implementation: `volcano-basic` - plotly Implements the **plotly** version of `volcano-basic`. **File:** `plots/volcano-basic/implementations/plotly.py` **Parent Issue:** #2924 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612779383)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/volcano-basic/implementations/plotly.py‎

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""" pyplots.ai
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volcano-basic: Volcano Plot for Statistical Significance
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Library: plotly 6.5.0 | Python 3.13.11
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Quality: 92/100 | Created: 2025-12-31
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"""
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import numpy as np
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import plotly.graph_objects as go
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# Data - Simulated differential gene expression results
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np.random.seed(42)
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n_genes = 500
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# Generate log2 fold changes (centered around 0, with some extreme values)
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log2_fold_change = np.random.normal(0, 1.5, n_genes)
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# Generate p-values (most non-significant, some significant)
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# Use a mixture: mostly high p-values, some low
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base_pvalues = np.random.beta(1, 3, n_genes)
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# Genes with larger fold changes tend to have lower p-values
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effect_boost = np.abs(log2_fold_change) / 5
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pvalues = base_pvalues * np.exp(-effect_boost * 3)
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pvalues = np.clip(pvalues, 1e-20, 1)
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# Transform to -log10 scale
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neg_log10_pvalue = -np.log10(pvalues)
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# Define significance thresholds
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fc_threshold = 1.0 # log2 fold change threshold (2-fold)
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pval_threshold = 0.05 # p-value threshold
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neg_log10_threshold = -np.log10(pval_threshold)
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# Classify points
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sig_up = (log2_fold_change > fc_threshold) & (neg_log10_pvalue > neg_log10_threshold)
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sig_down = (log2_fold_change < -fc_threshold) & (neg_log10_pvalue > neg_log10_threshold)
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non_sig = ~(sig_up | sig_down)
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# Gene names for top hits
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gene_names = [f"Gene_{i}" for i in range(n_genes)]
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# Create figure
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fig = go.Figure()
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# Non-significant points (gray)
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fig.add_trace(
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go.Scatter(
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x=log2_fold_change[non_sig],
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y=neg_log10_pvalue[non_sig],
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mode="markers",
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marker=dict(size=10, color="#888888", opacity=0.5),
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name="Not Significant",
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hovertemplate="%{text}<br>log2FC: %{x:.2f}<br>-log10(p): %{y:.2f}<extra></extra>",
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text=[gene_names[i] for i in np.where(non_sig)[0]],
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)
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)
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# Significant down-regulated (blue)
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fig.add_trace(
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go.Scatter(
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x=log2_fold_change[sig_down],
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y=neg_log10_pvalue[sig_down],
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mode="markers",
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marker=dict(size=12, color="#306998", opacity=0.8),
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name="Down-regulated",
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hovertemplate="%{text}<br>log2FC: %{x:.2f}<br>-log10(p): %{y:.2f}<extra></extra>",
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text=[gene_names[i] for i in np.where(sig_down)[0]],
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)
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)
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# Significant up-regulated (using a warm color - Python Yellow-ish orange)
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fig.add_trace(
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go.Scatter(
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x=log2_fold_change[sig_up],
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y=neg_log10_pvalue[sig_up],
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mode="markers",
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marker=dict(size=12, color="#D35400", opacity=0.8),
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name="Up-regulated",
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hovertemplate="%{text}<br>log2FC: %{x:.2f}<br>-log10(p): %{y:.2f}<extra></extra>",
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text=[gene_names[i] for i in np.where(sig_up)[0]],
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)
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)
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# Horizontal threshold line (p-value = 0.05)
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x_range = [min(log2_fold_change) - 0.5, max(log2_fold_change) + 0.5]
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fig.add_trace(
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go.Scatter(
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x=x_range,
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y=[neg_log10_threshold, neg_log10_threshold],
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mode="lines",
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line=dict(color="#333333", width=2, dash="dash"),
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name=f"p = {pval_threshold}",
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showlegend=True,
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)
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)
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# Vertical threshold lines (fold change = ±1)
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y_range = [0, max(neg_log10_pvalue) * 1.05]
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fig.add_trace(
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go.Scatter(
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x=[-fc_threshold, -fc_threshold],
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y=y_range,
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mode="lines",
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line=dict(color="#333333", width=2, dash="dash"),
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name=f"log2FC = -{fc_threshold}",
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showlegend=False,
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)
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)
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fig.add_trace(
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go.Scatter(
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x=[fc_threshold, fc_threshold],
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y=y_range,
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mode="lines",
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line=dict(color="#333333", width=2, dash="dash"),
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name=f"log2FC = {fc_threshold}",
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showlegend=True,
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)
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)
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# Label top significant genes
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top_indices = np.argsort(neg_log10_pvalue)[-5:] # Top 5 most significant
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annotations = []
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for idx in top_indices:
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if sig_up[idx] or sig_down[idx]:
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annotations.append(
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dict(
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x=log2_fold_change[idx],
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y=neg_log10_pvalue[idx],
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text=gene_names[idx],
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showarrow=True,
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arrowhead=2,
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arrowsize=1,
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arrowwidth=1.5,
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ax=30,
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ay=-30,
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font=dict(size=16, color="#333333"),
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)
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)
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# Update layout
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fig.update_layout(
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title=dict(text="volcano-basic · plotly · pyplots.ai", font=dict(size=28), x=0.5, xanchor="center"),
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xaxis=dict(
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title=dict(text="log₂ Fold Change", font=dict(size=22)),
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tickfont=dict(size=18),
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zeroline=True,
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zerolinewidth=1,
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zerolinecolor="#CCCCCC",
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gridcolor="rgba(0,0,0,0.1)",
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),
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yaxis=dict(
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title=dict(text="-log₁₀(p-value)", font=dict(size=22)), tickfont=dict(size=18), gridcolor="rgba(0,0,0,0.1)"
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),
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template="plotly_white",
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legend=dict(
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font=dict(size=18), x=0.02, y=0.98, bgcolor="rgba(255,255,255,0.8)", bordercolor="#CCCCCC", borderwidth=1
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),
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annotations=annotations,
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margin=dict(l=80, r=40, t=80, b=80),
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)
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# Save as PNG (4800 x 2700 px)
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fig.write_image("plot.png", width=1600, height=900, scale=3)
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# Save interactive HTML version
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fig.write_html("plot.html", include_plotlyjs=True, full_html=True)

‎plots/volcano-basic/metadata/plotly.yaml‎

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library: plotly
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specification_id: volcano-basic
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created: '2025-12-31T05:32:16Z'
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updated: '2025-12-31T05:42:24Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20612779383
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issue: 2924
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python_version: 3.13.11
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library_version: 6.5.0
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preview_url: https://storage.googleapis.com/pyplots-images/plots/volcano-basic/plotly/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/volcano-basic/plotly/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/volcano-basic/plotly/plot.html
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quality_score: 92
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review:
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strengths:
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- Excellent implementation of volcano plot with all required threshold lines and
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color coding
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- Proper use of Plotly hover templates showing gene name, fold change, and p-value
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- Clean three-color scheme (gray/blue/orange) that is colorblind accessible
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- Appropriate marker sizing and alpha transparency for the data density
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- Gene annotations with arrows for top significant hits
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- Both PNG and HTML outputs generated correctly
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weaknesses:
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- Legend includes threshold line entries (p = 0.05, log2FC = 1.0) which adds visual
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clutter; threshold lines are self-explanatory from context
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- Some overlap in the dense central gray point cluster could benefit from slightly
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smaller markers or more transparency
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- Could utilize more Plotly-specific features like updatemenus or rangeslider for
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enhanced interactivity

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