{"spec_id":"volcano-basic","library":"plotly","language":"python","code":"\"\"\" anyplot.ai\nvolcano-basic: Volcano Plot for Statistical Significance\nLibrary: plotly 6.7.0 | Python 3.13.13\nQuality: 93/100 | Updated: 2026-05-14\n\"\"\"\n\nimport os\n\nimport numpy as np\nimport plotly.graph_objects as go\n\n\n# Theme tokens\nTHEME = os.getenv(\"ANYPLOT_THEME\", \"light\")\nPAGE_BG = \"#FAF8F1\" if THEME == \"light\" else \"#1A1A17\"\nELEVATED_BG = \"#FFFDF6\" if THEME == \"light\" else \"#242420\"\nINK = \"#1A1A17\" if THEME == \"light\" else \"#F0EFE8\"\nINK_SOFT = \"#4A4A44\" if THEME == \"light\" else \"#B8B7B0\"\nGRID = \"rgba(26,26,23,0.10)\" if THEME == \"light\" else \"rgba(240,239,232,0.10)\"\n\n# Okabe-Ito palette\nOKABE_ITO_VERMILLION = \"#C475FD\"  # Position 2 - red/orange for up-regulated\nOKABE_ITO_BLUE = \"#4467A3\"  # Position 3 - blue for down-regulated\n\n# Data - Simulated differential gene expression results\nnp.random.seed(42)\nn_genes = 500\n\n# Generate log2 fold changes\nlog2_fold_change = np.random.normal(0, 1.5, n_genes)\n\n# Generate p-values (most non-significant, some significant)\nbase_pvalues = np.random.beta(1, 3, n_genes)\neffect_boost = np.abs(log2_fold_change) / 5\npvalues = base_pvalues * np.exp(-effect_boost * 3)\npvalues = np.clip(pvalues, 1e-20, 1)\n\n# Transform to -log10 scale\nneg_log10_pvalue = -np.log10(pvalues)\n\n# Define significance thresholds\nfc_threshold = 1.0  # log2 fold change threshold\npval_threshold = 0.05\nneg_log10_threshold = -np.log10(pval_threshold)\n\n# Classify points\nsig_up = (log2_fold_change > fc_threshold) & (neg_log10_pvalue > neg_log10_threshold)\nsig_down = (log2_fold_change < -fc_threshold) & (neg_log10_pvalue > neg_log10_threshold)\nnon_sig = ~(sig_up | sig_down)\n\n# Gene names\ngene_names = [f\"Gene_{i}\" for i in range(n_genes)]\n\n# Create figure\nfig = go.Figure()\n\n# Non-significant points (adaptive neutral gray)\nfig.add_trace(\n    go.Scatter(\n        x=log2_fold_change[non_sig],\n        y=neg_log10_pvalue[non_sig],\n        mode=\"markers\",\n        marker=dict(size=9, color=INK_SOFT, opacity=0.4),\n        name=\"Not Significant\",\n        hovertemplate=\"%{text}<br>log₂FC: %{x:.2f}<br>-log₁₀(p): %{y:.2f}<extra></extra>\",\n        text=[gene_names[i] for i in np.where(non_sig)[0]],\n    )\n)\n\n# Significant down-regulated (Okabe-Ito blue)\nfig.add_trace(\n    go.Scatter(\n        x=log2_fold_change[sig_down],\n        y=neg_log10_pvalue[sig_down],\n        mode=\"markers\",\n        marker=dict(size=12, color=OKABE_ITO_BLUE, opacity=0.8),\n        name=\"Down-regulated\",\n        hovertemplate=\"%{text}<br>log₂FC: %{x:.2f}<br>-log₁₀(p): %{y:.2f}<extra></extra>\",\n        text=[gene_names[i] for i in np.where(sig_down)[0]],\n    )\n)\n\n# Significant up-regulated (Okabe-Ito vermillion)\nfig.add_trace(\n    go.Scatter(\n        x=log2_fold_change[sig_up],\n        y=neg_log10_pvalue[sig_up],\n        mode=\"markers\",\n        marker=dict(size=12, color=OKABE_ITO_VERMILLION, opacity=0.8),\n        name=\"Up-regulated\",\n        hovertemplate=\"%{text}<br>log₂FC: %{x:.2f}<br>-log₁₀(p): %{y:.2f}<extra></extra>\",\n        text=[gene_names[i] for i in np.where(sig_up)[0]],\n    )\n)\n\n# Horizontal threshold line (p-value = 0.05)\nx_range = [min(log2_fold_change) - 0.5, max(log2_fold_change) + 0.5]\nfig.add_trace(\n    go.Scatter(\n        x=x_range,\n        y=[neg_log10_threshold, neg_log10_threshold],\n        mode=\"lines\",\n        line=dict(color=INK_SOFT, width=2, dash=\"dash\"),\n        showlegend=False,\n    )\n)\n\n# Vertical threshold lines (fold change = ±1)\ny_range = [0, max(neg_log10_pvalue) * 1.05]\nfig.add_trace(\n    go.Scatter(\n        x=[-fc_threshold, -fc_threshold],\n        y=y_range,\n        mode=\"lines\",\n        line=dict(color=INK_SOFT, width=2, dash=\"dash\"),\n        showlegend=False,\n    )\n)\nfig.add_trace(\n    go.Scatter(\n        x=[fc_threshold, fc_threshold],\n        y=y_range,\n        mode=\"lines\",\n        line=dict(color=INK_SOFT, width=2, dash=\"dash\"),\n        showlegend=False,\n    )\n)\n\n# Label top significant genes\ntop_indices = np.argsort(neg_log10_pvalue)[-5:]\nannotations = []\nfor idx in top_indices:\n    if sig_up[idx] or sig_down[idx]:\n        annotations.append(\n            dict(\n                x=log2_fold_change[idx],\n                y=neg_log10_pvalue[idx],\n                text=gene_names[idx],\n                showarrow=True,\n                arrowhead=2,\n                arrowsize=1,\n                arrowwidth=1.5,\n                ax=30,\n                ay=-30,\n                font=dict(size=16, color=INK),\n            )\n        )\n\n# Update layout\nfig.update_layout(\n    title=dict(text=\"volcano-basic · plotly · anyplot.ai\", font=dict(size=28, color=INK), x=0.5, xanchor=\"center\"),\n    xaxis=dict(\n        title=dict(text=\"log₂ Fold Change\", font=dict(size=22, color=INK)),\n        tickfont=dict(size=18, color=INK_SOFT),\n        zeroline=True,\n        zerolinewidth=1,\n        zerolinecolor=INK_SOFT,\n        gridcolor=GRID,\n        linecolor=INK_SOFT,\n    ),\n    yaxis=dict(\n        title=dict(text=\"-log₁₀(p-value)\", font=dict(size=22, color=INK)),\n        tickfont=dict(size=18, color=INK_SOFT),\n        gridcolor=GRID,\n        linecolor=INK_SOFT,\n    ),\n    paper_bgcolor=PAGE_BG,\n    plot_bgcolor=PAGE_BG,\n    font=dict(color=INK),\n    legend=dict(\n        font=dict(size=18, color=INK_SOFT), x=0.02, y=0.98, bgcolor=ELEVATED_BG, bordercolor=INK_SOFT, borderwidth=1\n    ),\n    annotations=annotations,\n    margin=dict(l=80, r=40, t=80, b=80),\n)\n\n# Save as PNG (4800 x 2700 px)\nfig.write_image(f\"plot-{THEME}.png\", width=1600, height=900, scale=3)\n\n# Save interactive HTML version\nfig.write_html(f\"plot-{THEME}.html\", include_plotlyjs=\"cdn\")\n"}