feat(plotnine): implement volcano-basic (#2943)

## Implementation: `volcano-basic` - plotnine

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

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

**Parent Issue:** #2924

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612783423)*

---------

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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/plotnine.py

@@ -0,0 +1,105 @@
+""" pyplots.ai
+volcano-basic: Volcano Plot for Statistical Significance
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    element_text,
+    geom_hline,
+    geom_point,
+    geom_text,
+    geom_vline,
+    ggplot,
+    labs,
+    scale_color_manual,
+    theme,
+    theme_minimal,
+)
+
+
+# Data - simulated differential gene expression results
+np.random.seed(42)
+n_genes = 500
+
+# Generate log2 fold changes (centered around 0 with some outliers)
+log2_fold_change = np.concatenate(
+    [
+        np.random.normal(0, 0.8, 400),  # Most genes have small changes
+        np.random.normal(-2.5, 0.5, 50),  # Down-regulated genes
+        np.random.normal(2.5, 0.5, 50),  # Up-regulated genes
+    ]
+)
+
+# Generate p-values with a realistic range (avoiding extreme values)
+pvalues = np.concatenate(
+    [
+        np.random.uniform(0.05, 1.0, 400),  # Most genes not significant
+        np.random.uniform(0.0001, 0.01, 50),  # Down-regulated significant
+        np.random.uniform(0.0001, 0.01, 50),  # Up-regulated significant
+    ]
+)
+
+neg_log10_pvalue = -np.log10(pvalues)
+
+# Create gene labels
+gene_labels = [f"Gene_{i + 1}" for i in range(n_genes)]
+
+# Determine significance status based on thresholds
+# Significant: p-value < 0.05 (neg_log10 > 1.3) AND |log2FC| > 1
+significance_threshold = -np.log10(0.05)  # ~1.3
+fold_change_threshold = 1.0
+
+status = []
+for fc, nlp in zip(log2_fold_change, neg_log10_pvalue, strict=True):
+    if nlp > significance_threshold and fc > fold_change_threshold:
+        status.append("Up-regulated")
+    elif nlp > significance_threshold and fc < -fold_change_threshold:
+        status.append("Down-regulated")
+    else:
+        status.append("Not significant")
+
+# Create DataFrame
+df = pd.DataFrame(
+    {
+        "log2_fold_change": log2_fold_change,
+        "neg_log10_pvalue": neg_log10_pvalue,
+        "label": gene_labels,
+        "status": pd.Categorical(status, categories=["Down-regulated", "Not significant", "Up-regulated"]),
+    }
+)
+
+# Identify top genes to label (top 3 by significance in each direction to avoid overlap)
+df_up = df[df["status"] == "Up-regulated"].nlargest(3, "neg_log10_pvalue")
+df_down = df[df["status"] == "Down-regulated"].nlargest(3, "neg_log10_pvalue")
+df_labels = pd.concat([df_up, df_down])
+
+# Create volcano plot
+plot = (
+    ggplot(df, aes(x="log2_fold_change", y="neg_log10_pvalue", color="status"))
+    + geom_point(size=3, alpha=0.7)
+    + geom_hline(yintercept=significance_threshold, linetype="dashed", color="#333333", size=0.8)
+    + geom_vline(xintercept=-fold_change_threshold, linetype="dashed", color="#333333", size=0.8)
+    + geom_vline(xintercept=fold_change_threshold, linetype="dashed", color="#333333", size=0.8)
+    + geom_text(data=df_labels, mapping=aes(label="label"), size=10, nudge_y=0.3, color="#333333")
+    + scale_color_manual(values={"Down-regulated": "#306998", "Not significant": "#888888", "Up-regulated": "#D62728"})
+    + labs(
+        x="Log2 Fold Change", y="-Log10(p-value)", title="volcano-basic · plotnine · pyplots.ai", color="Significance"
+    )
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        text=element_text(size=14),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        plot_title=element_text(size=24),
+        legend_text=element_text(size=16),
+        legend_title=element_text(size=18),
+    )
+)
+
+# Save plot
+plot.save("plot.png", dpi=300)

plots/volcano-basic/metadata/plotnine.yaml

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+library: plotnine
+specification_id: volcano-basic
+created: '2025-12-31T05:32:18Z'
+updated: '2025-12-31T05:40:21Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612783423
+issue: 2924
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/volcano-basic/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/volcano-basic/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of the volcano plot specification with all required features
+    (threshold lines, color coding, gene labels)
+  - Clean grammar of graphics approach using plotnine ggplot2-style syntax
+  - Good data simulation that realistically represents differential gene expression
+    results
+  - Text sizes properly configured for 4800x2700 output
+  - Proper use of categorical coloring with meaningful significance categories
+  weaknesses:
+  - Minor label overlap between Gene_469 and Gene_478 on the right side of the plot
+  - Could use colorblind-safe palette instead of red-blue (consider using orange or
+    different saturation)
+  - Grid lines not visible (theme_minimal removes them) which could aid reading values