feat(seaborn): implement volcano-basic (#2937)

## Implementation: `volcano-basic` - seaborn

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

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

**Parent Issue:** #2924

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:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612779597)*

---------

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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/seaborn.py

@@ -0,0 +1,100 @@
+""" pyplots.ai
+volcano-basic: Volcano Plot for Statistical Significance
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+# Data: Simulated differential expression results
+np.random.seed(42)
+n_genes = 500
+
+# Generate log2 fold changes (centered around 0, some extreme values)
+log2_fold_change = np.concatenate(
+    [
+        np.random.normal(0, 0.5, n_genes - 60),  # Most genes: no change
+        np.random.normal(-2.5, 0.5, 30),  # Down-regulated
+        np.random.normal(2.5, 0.5, 30),  # Up-regulated
+    ]
+)
+
+# Generate p-values (correlated with fold change magnitude for realism)
+base_pvalues = 10 ** (-np.abs(log2_fold_change) * np.random.uniform(0.5, 2, n_genes))
+base_pvalues = np.clip(base_pvalues, 1e-10, 1.0)
+neg_log10_pvalue = -np.log10(base_pvalues)
+
+# Define significance thresholds
+pval_threshold = 1.3  # -log10(0.05)
+fc_threshold = 1.0  # log2(2) = 1
+
+# Categorize genes using numpy vectorized conditions
+categories = np.where(
+    neg_log10_pvalue < pval_threshold,
+    "Not Significant",
+    np.where(
+        log2_fold_change > fc_threshold,
+        "Up-regulated",
+        np.where(log2_fold_change < -fc_threshold, "Down-regulated", "Not Significant"),
+    ),
+)
+
+# Create DataFrame
+df = pd.DataFrame({"log2_fold_change": log2_fold_change, "neg_log10_pvalue": neg_log10_pvalue, "category": categories})
+
+# Sort so significant points are plotted on top
+category_order = {"Not Significant": 0, "Down-regulated": 1, "Up-regulated": 2}
+df["order"] = df["category"].map(category_order)
+df = df.sort_values("order")
+
+# Color palette matching specification
+palette = {
+    "Not Significant": "#888888",
+    "Down-regulated": "#306998",  # Python Blue
+    "Up-regulated": "#E63946",  # Red for up-regulated
+}
+
+# Create figure
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Scatter plot using seaborn
+sns.scatterplot(
+    data=df,
+    x="log2_fold_change",
+    y="neg_log10_pvalue",
+    hue="category",
+    hue_order=["Not Significant", "Down-regulated", "Up-regulated"],
+    palette=palette,
+    s=100,
+    alpha=0.7,
+    edgecolor="none",
+    ax=ax,
+    legend=True,
+)
+
+# Threshold lines
+ax.axhline(y=pval_threshold, color="#333333", linestyle="--", linewidth=2, alpha=0.7)
+ax.axvline(x=fc_threshold, color="#333333", linestyle="--", linewidth=2, alpha=0.7)
+ax.axvline(x=-fc_threshold, color="#333333", linestyle="--", linewidth=2, alpha=0.7)
+
+# Add threshold annotations
+ax.text(ax.get_xlim()[1] - 0.3, pval_threshold + 0.3, "p = 0.05", fontsize=14, ha="right", color="#333333")
+ax.text(fc_threshold + 0.1, ax.get_ylim()[1] - 0.5, "FC = 2", fontsize=14, ha="left", color="#333333")
+ax.text(-fc_threshold - 0.1, ax.get_ylim()[1] - 0.5, "FC = 0.5", fontsize=14, ha="right", color="#333333")
+
+# Labels and styling
+ax.set_xlabel("Log2 Fold Change", fontsize=20)
+ax.set_ylabel("-Log10(p-value)", fontsize=20)
+ax.set_title("volcano-basic · seaborn · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3, linestyle="--")
+
+# Legend styling
+ax.legend(title="Significance", fontsize=14, title_fontsize=16, loc="upper right", framealpha=0.9)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/volcano-basic/metadata/seaborn.yaml

@@ -0,0 +1,29 @@
+library: seaborn
+specification_id: volcano-basic
+created: '2025-12-31T05:31:24Z'
+updated: '2025-12-31T05:37:02Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612779597
+issue: 2924
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/volcano-basic/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/volcano-basic/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of the classic volcano plot pattern with clear visual
+    separation of significance categories
+  - Proper threshold annotations (p=0.05, FC=2, FC=0.5) provide context for the significance
+    cutoffs
+  - Clean data generation with realistic bimodal distribution for differentially expressed
+    genes
+  - Good use of seaborn hue parameter with custom palette matching the specification
+    colors
+  - Sorting mechanism ensures significant points are plotted on top and remain visible
+  weaknesses:
+  - Color palette could be more colorblind-friendly (consider using blue-orange instead
+    of blue-red)
+  - Missing optional gene labels for top significant features mentioned in the spec
+    notes