feat(plotly): implement volcano-basic

plots/volcano-basic/implementations/plotly.py

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

plots/volcano-basic/metadata/plotly.yaml

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