feat(plotnine): implement manhattan-gwas

plots/manhattan-gwas/implementations/plotnine.py

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+""" pyplots.ai
+manhattan-gwas: Manhattan Plot for GWAS
+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_blank,
+    element_text,
+    geom_hline,
+    geom_point,
+    ggplot,
+    labs,
+    scale_color_manual,
+    scale_x_continuous,
+    scale_y_continuous,
+    theme,
+    theme_minimal,
+)
+
+
+# Data - Simulated GWAS results with significant peaks
+np.random.seed(42)
+
+# Chromosome sizes (approximate in Mb, scaled for visualization)
+chr_sizes = {
+    "1": 249,
+    "2": 243,
+    "3": 198,
+    "4": 191,
+    "5": 182,
+    "6": 171,
+    "7": 159,
+    "8": 145,
+    "9": 138,
+    "10": 134,
+    "11": 135,
+    "12": 133,
+    "13": 115,
+    "14": 107,
+    "15": 102,
+    "16": 90,
+    "17": 83,
+    "18": 80,
+    "19": 59,
+    "20": 64,
+    "21": 47,
+    "22": 51,
+}
+
+# Generate SNPs per chromosome (proportional to size)
+chromosomes = list(chr_sizes.keys())
+snp_data = []
+
+# Cumulative positions for x-axis
+cumulative_offset = 0
+chr_offsets = {}
+chr_midpoints = {}
+
+for chrom in chromosomes:
+    chr_offsets[chrom] = cumulative_offset
+    size = chr_sizes[chrom]
+    n_snps = int(size * 40)  # ~40 SNPs per Mb = ~8000 total
+
+    # Generate positions
+    positions = np.sort(np.random.uniform(0, size * 1e6, n_snps))
+
+    # Generate p-values (mostly non-significant, with some peaks)
+    p_values = np.random.uniform(0.001, 1, n_snps)
+
+    # Add significant peaks on some chromosomes
+    if chrom in ["2", "6", "11", "17"]:
+        # Add 20-40 highly significant SNPs
+        n_sig = np.random.randint(20, 40)
+        peak_idx = np.random.choice(n_snps, n_sig, replace=False)
+        p_values[peak_idx] = 10 ** np.random.uniform(-10, -7.3, n_sig)
+
+    # Add some suggestive signals on other chromosomes
+    if chrom in ["4", "9", "15", "20"]:
+        n_sug = np.random.randint(10, 20)
+        sug_idx = np.random.choice(n_snps, n_sug, replace=False)
+        p_values[sug_idx] = 10 ** np.random.uniform(-7, -5, n_sug)
+
+    # Calculate cumulative position
+    cumulative_positions = positions + cumulative_offset
+
+    chr_midpoints[chrom] = cumulative_offset + (size * 1e6) / 2
+
+    for i in range(n_snps):
+        snp_data.append(
+            {
+                "chromosome": chrom,
+                "position": positions[i],
+                "cumulative_pos": cumulative_positions[i],
+                "p_value": p_values[i],
+            }
+        )
+
+    cumulative_offset += size * 1e6
+
+# Create DataFrame
+df = pd.DataFrame(snp_data)
+
+# Calculate -log10(p-value)
+df["neg_log_p"] = -np.log10(df["p_value"])
+
+# Assign alternating colors based on chromosome
+chr_order = {c: i for i, c in enumerate(chromosomes)}
+df["chr_num"] = df["chromosome"].map(chr_order)
+df["color_group"] = df["chr_num"].apply(lambda x: "odd" if x % 2 == 0 else "even")
+
+# Threshold lines
+genome_wide_threshold = -np.log10(5e-8)  # ~7.3
+suggestive_threshold = -np.log10(1e-5)  # 5
+
+# Identify top SNPs for potential labeling (above genome-wide significance)
+df["significant"] = df["neg_log_p"] > genome_wide_threshold
+
+# Create chromosome tick positions and labels
+chr_ticks = [chr_midpoints[c] / 1e6 for c in chromosomes]  # Convert to Mb for display
+chr_labels = chromosomes
+
+# Scale positions to Mb for cleaner axis
+df["cumulative_pos_mb"] = df["cumulative_pos"] / 1e6
+
+# Plot
+plot = (
+    ggplot(df, aes(x="cumulative_pos_mb", y="neg_log_p", color="color_group"))
+    + geom_point(size=1.2, alpha=0.7, show_legend=False)
+    + geom_hline(yintercept=genome_wide_threshold, linetype="dashed", color="#E31A1C", size=1)
+    + geom_hline(yintercept=suggestive_threshold, linetype="dotted", color="#FF7F00", size=0.8)
+    + scale_color_manual(values={"odd": "#306998", "even": "#636363"})
+    + scale_x_continuous(breaks=chr_ticks, labels=chr_labels)
+    + scale_y_continuous(limits=(0, max(df["neg_log_p"]) * 1.05))
+    + labs(x="Chromosome", y="-log₁₀(p-value)", title="manhattan-gwas · plotnine · pyplots.ai")
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        plot_title=element_text(size=24, ha="center"),
+        axis_title_x=element_text(size=20),
+        axis_title_y=element_text(size=20),
+        axis_text_x=element_text(size=12),
+        axis_text_y=element_text(size=16),
+        panel_grid_major_x=element_blank(),
+        panel_grid_minor=element_blank(),
+    )
+)
+
+# Save
+plot.save("plot.png", dpi=300, width=16, height=9)

plots/manhattan-gwas/metadata/plotnine.yaml

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+library: plotnine
+specification_id: manhattan-gwas
+created: '2025-12-31T05:32:21Z'
+updated: '2025-12-31T05:41:05Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612792880
+issue: 2925
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/manhattan-gwas/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/manhattan-gwas/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent use of plotnine grammar of graphics with clean layered syntax
+  - Well-implemented alternating chromosome colors using factor mapping
+  - Both genome-wide and suggestive threshold lines included with appropriate styling
+  - Good point sizing and alpha for the data density
+  - Chromosome labels properly centered at midpoints
+  - Clean title format following spec requirements
+  weaknesses:
+  - X-axis chromosome labels 19-22 are slightly crowded; consider rotating or using
+    abbreviated labels
+  - Could highlight or label top significant SNPs as noted in spec