feat(seaborn): implement manhattan-gwas

plots/manhattan-gwas/implementations/seaborn.py

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+""" pyplots.ai
+manhattan-gwas: Manhattan Plot for GWAS
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-31
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+# Data - Simulate GWAS data with realistic structure
+np.random.seed(42)
+
+# Define chromosomes with approximate sizes (in Mb)
+chromosomes = [str(i) for i in range(1, 23)]
+chrom_sizes = [250, 243, 198, 190, 182, 171, 159, 145, 138, 133, 135, 133, 114, 107, 102, 90, 83, 80, 59, 64, 47, 51]
+
+# Generate SNPs for each chromosome
+data = []
+cumulative_pos = 0
+chrom_centers = {}
+chrom_boundaries = [0]
+
+for chrom, size in zip(chromosomes, chrom_sizes, strict=True):
+    # Number of SNPs proportional to chromosome size
+    n_snps = int(size * 40)  # ~40 SNPs per Mb, total ~10k SNPs
+
+    # Random positions along chromosome
+    positions = np.sort(np.random.randint(0, size * 1_000_000, n_snps))
+
+    # Generate p-values - mostly non-significant with some peaks
+    # Use beta distribution to get realistic p-value distribution
+    p_values = np.random.beta(1, 1, n_snps)
+
+    # Add significant peaks on specific chromosomes
+    if chrom == "6":  # Major peak on chr6 (like MHC region)
+        peak_region = (positions > 25_000_000) & (positions < 35_000_000)
+        p_values[peak_region] = 10 ** (-np.random.uniform(7, 12, peak_region.sum()))
+    elif chrom == "11":  # Moderate peak
+        peak_region = (positions > 60_000_000) & (positions < 70_000_000)
+        p_values[peak_region] = 10 ** (-np.random.uniform(6, 9, peak_region.sum()))
+    elif chrom == "2":  # Smaller peak
+        peak_region = (positions > 100_000_000) & (positions < 110_000_000)
+        p_values[peak_region] = 10 ** (-np.random.uniform(5.5, 8, peak_region.sum()))
+
+    # Calculate cumulative position
+    cumulative_positions = positions + cumulative_pos
+
+    # Store center for axis label
+    chrom_centers[chrom] = cumulative_pos + (size * 1_000_000) / 2
+
+    for pos, cum_pos, pval in zip(positions, cumulative_positions, p_values, strict=True):
+        data.append(
+            {
+                "chromosome": chrom,
+                "position": pos,
+                "cumulative_position": cum_pos,
+                "p_value": pval,
+                "neg_log_p": -np.log10(pval),
+            }
+        )
+
+    cumulative_pos += size * 1_000_000
+    chrom_boundaries.append(cumulative_pos)
+
+df = pd.DataFrame(data)
+
+# Create alternating color groups for chromosomes
+df["color_group"] = df["chromosome"].apply(lambda x: int(x) % 2)
+
+# Plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Create custom palette - Python Blue and a muted gray
+palette = {0: "#306998", 1: "#8B9DC3"}
+
+# Plot points using seaborn scatterplot with hue for alternating colors
+sns.scatterplot(
+    data=df,
+    x="cumulative_position",
+    y="neg_log_p",
+    hue="color_group",
+    palette=palette,
+    s=15,
+    alpha=0.7,
+    edgecolor="none",
+    legend=False,
+    ax=ax,
+)
+
+# Highlight significant SNPs (above genome-wide threshold)
+significant = df[df["neg_log_p"] > 7.3]
+if len(significant) > 0:
+    ax.scatter(
+        significant["cumulative_position"],
+        significant["neg_log_p"],
+        c="#FFD43B",  # Python Yellow for significant hits
+        s=40,
+        edgecolor="black",
+        linewidth=0.5,
+        zorder=5,
+    )
+
+# Genome-wide significance threshold
+ax.axhline(y=7.3, color="#D62728", linestyle="--", linewidth=2, label="Genome-wide significance (p < 5×10⁻⁸)")
+
+# Suggestive threshold
+ax.axhline(y=5, color="#7F7F7F", linestyle=":", linewidth=1.5, label="Suggestive (p < 1×10⁻⁵)")
+
+# Set x-axis ticks at chromosome centers
+ax.set_xticks([chrom_centers[c] for c in chromosomes])
+ax.set_xticklabels(chromosomes)
+
+# Styling
+ax.set_xlabel("Chromosome", fontsize=20)
+ax.set_ylabel("-log₁₀(p-value)", fontsize=20)
+ax.set_title("manhattan-gwas · seaborn · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=14)
+ax.tick_params(axis="x", labelsize=12)
+
+# Set axis limits
+ax.set_xlim(0, cumulative_pos)
+ax.set_ylim(0, max(df["neg_log_p"]) * 1.05)
+
+# Remove top and right spines for cleaner look
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+
+# Add legend
+ax.legend(loc="upper right", fontsize=14, framealpha=0.9)
+
+# Subtle grid on y-axis only
+ax.yaxis.grid(True, alpha=0.3, linestyle="-")
+ax.xaxis.grid(False)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/manhattan-gwas/metadata/seaborn.yaml

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+library: seaborn
+specification_id: manhattan-gwas
+created: '2025-12-31T05:32:05Z'
+updated: '2025-12-31T05:38:41Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612790719
+issue: 2925
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/manhattan-gwas/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/manhattan-gwas/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent biological realism with chromosome-specific significant peaks mimicking
+    real GWAS patterns (MHC region on chr6)
+  - Clean visual design with alternating chromosome colors and clear significance
+    thresholds
+  - Proper handling of dense data with appropriate marker size and transparency
+  - Correct title format following pyplots.ai conventions
+  - Yellow highlighting of significant SNPs provides excellent visual emphasis
+  weaknesses:
+  - Y-axis grid could be styled more subtly (current alpha=0.3 is slightly prominent)
+  - Significant SNPs highlighting uses raw matplotlib instead of seaborn; could use
+    a second scatterplot call for consistency