feat(letsplot): implement volcano-basic (#2960)

## Implementation: `volcano-basic` - letsplot

Implements the **letsplot** version of `volcano-basic`.

**File:** `plots/volcano-basic/implementations/letsplot.py`

**Parent Issue:** #2924

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612785486)*

---------

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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/volcano-basic/implementations/letsplot.py

@@ -0,0 +1,81 @@
+""" pyplots.ai
+volcano-basic: Volcano Plot for Statistical Significance
+Library: letsplot 4.8.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import *
+
+
+LetsPlot.setup_html()
+
+# Data - Simulated differential expression results
+np.random.seed(42)
+n_genes = 500
+
+# Generate log2 fold changes (mostly near zero with some extremes)
+log2_fc = np.concatenate(
+    [
+        np.random.normal(0, 0.4, n_genes - 100),  # Unchanged genes
+        np.random.normal(2.2, 0.6, 50),  # Up-regulated
+        np.random.normal(-2.2, 0.6, 50),  # Down-regulated
+    ]
+)
+
+# Generate p-values (strongly correlated with fold change magnitude)
+# Higher fold change = lower p-value (more significant)
+neg_log10_pval = np.zeros(n_genes)
+for i, fc in enumerate(log2_fc):
+    if abs(fc) > 1.5:  # Large fold changes get significant p-values
+        neg_log10_pval[i] = np.random.uniform(1.5, 3.5)
+    elif abs(fc) > 1.0:  # Moderate fold changes get borderline p-values
+        neg_log10_pval[i] = np.random.uniform(0.8, 2.0)
+    else:  # Small fold changes get non-significant p-values
+        neg_log10_pval[i] = np.random.uniform(0.1, 1.5)
+
+# Determine significance status
+p_threshold = 1.3  # -log10(0.05)
+fc_threshold = 1.0  # log2(2) = 1
+
+significance = []
+for fc, nlp in zip(log2_fc, neg_log10_pval):
+    if nlp > p_threshold and fc > fc_threshold:
+        significance.append("Up-regulated")
+    elif nlp > p_threshold and fc < -fc_threshold:
+        significance.append("Down-regulated")
+    else:
+        significance.append("Not significant")
+
+# Create DataFrame
+df = pd.DataFrame({"log2_fold_change": log2_fc, "neg_log10_pvalue": neg_log10_pval, "significance": significance})
+
+# Create volcano plot
+plot = (
+    ggplot(df, aes(x="log2_fold_change", y="neg_log10_pvalue", color="significance"))
+    + geom_point(aes(color="significance"), size=4, alpha=0.7)
+    + geom_hline(yintercept=p_threshold, linetype="dashed", color="#666666", size=1)
+    + geom_vline(xintercept=-fc_threshold, linetype="dashed", color="#666666", size=1)
+    + geom_vline(xintercept=fc_threshold, linetype="dashed", color="#666666", size=1)
+    + scale_color_manual(
+        values=["#306998", "#888888", "#DC2626"], breaks=["Down-regulated", "Not significant", "Up-regulated"]
+    )
+    + labs(x="Log2 Fold Change", y="-Log10(p-value)", title="volcano-basic · letsplot · pyplots.ai", color="Status")
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=24, face="bold"),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        legend_title=element_text(size=18),
+        legend_text=element_text(size=16),
+        legend_position="right",
+    )
+    + ggsize(1600, 900)
+)
+
+# Save as PNG (scale=3 gives 4800x2700)
+ggsave(plot, "plot.png", path=".", scale=3)
+
+# Save as HTML for interactivity
+ggsave(plot, "plot.html", path=".")

plots/volcano-basic/metadata/letsplot.yaml

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+library: letsplot
+specification_id: volcano-basic
+created: '2025-12-31T05:34:59Z'
+updated: '2025-12-31T05:46:59Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612785486
+issue: 2924
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/volcano-basic/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/volcano-basic/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/volcano-basic/letsplot/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Clear visual distinction between significance categories using blue/gray/red color
+    coding
+  - Proper threshold lines at -log10(0.05) and ±1 fold change as specified
+  - Uses lets-plot grammar of graphics effectively with ggplot + geom_point + scale_color_manual
+  - Good data generation with realistic volcano plot distribution - appropriate mix
+    of significant and non-significant genes
+  - Correct title format following spec-id · library · pyplots.ai convention
+  weaknesses:
+  - Legend title says Status instead of Significance for better clarity