feat(pygal): implement ma-differential-expression (#5081)

## Implementation: `ma-differential-expression` - pygal

Implements the **pygal** version of `ma-differential-expression`.

**File:** `plots/ma-differential-expression/implementations/pygal.py`

**Parent Issue:** #4420

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/23339077886)*

---------

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

Author: github-actions[bot]

plots/ma-differential-expression/implementations/pygal.py

@@ -0,0 +1,216 @@
+""" pyplots.ai
+ma-differential-expression: MA Plot for Differential Expression
+Library: pygal 3.1.0 | Python 3.14.3
+Quality: 84/100 | Created: 2026-03-20
+"""
+
+import numpy as np
+import pygal
+from pygal.style import Style
+
+
+# Data - Simulated RNA-seq differential expression results
+np.random.seed(42)
+n_genes = 15000
+
+# Mean expression (A values) - log2 scale, typical RNA-seq range
+mean_expression = np.random.exponential(scale=3, size=n_genes) + 1
+
+# Log fold change (M values) - most genes near zero, some truly differentially expressed
+log_fold_change = np.random.normal(0, 0.3, n_genes)
+
+# Add truly differentially expressed genes (~8% upregulated, ~7% downregulated)
+n_up = 1200
+n_down = 1050
+up_idx = np.random.choice(n_genes, n_up, replace=False)
+remaining = np.setdiff1d(np.arange(n_genes), up_idx)
+down_idx = np.random.choice(remaining, n_down, replace=False)
+
+log_fold_change[up_idx] = np.random.normal(2.5, 0.8, n_up)
+log_fold_change[down_idx] = np.random.normal(-2.2, 0.7, n_down)
+
+# Simulate p-values (significant for DE genes, noise for others)
+p_values = np.ones(n_genes)
+p_values[up_idx] = 10 ** (-np.random.uniform(2, 10, n_up))
+p_values[down_idx] = 10 ** (-np.random.uniform(2, 10, n_down))
+noise_idx = np.setdiff1d(np.arange(n_genes), np.concatenate([up_idx, down_idx]))
+p_values[noise_idx] = np.random.uniform(0.01, 1.0, len(noise_idx))
+
+# Significance threshold
+significant = p_values < 0.05
+
+# Gene names for top DE genes
+gene_names = [f"Gene{i}" for i in range(n_genes)]
+top_genes = ["BRCA1", "TP53", "MYC", "EGFR", "KRAS", "PTEN", "CDK2", "RB1", "AKT1", "VEGFA"]
+top_idx = np.argsort(p_values)[: len(top_genes)]
+for i, name in zip(top_idx, top_genes, strict=False):
+    gene_names[i] = name
+
+# LOESS-like smoothing curve (binned moving average with additional smoothing)
+sort_order = np.argsort(mean_expression)
+sorted_a = mean_expression[sort_order]
+sorted_m = log_fold_change[sort_order]
+
+n_bins = 30
+bin_edges = np.percentile(sorted_a, np.linspace(0, 100, n_bins + 1))
+raw_x = []
+raw_y = []
+for b in range(n_bins):
+    mask = (sorted_a >= bin_edges[b]) & (sorted_a < bin_edges[b + 1])
+    if mask.sum() > 20:
+        raw_x.append(float(np.median(sorted_a[mask])))
+        raw_y.append(float(np.mean(sorted_m[mask])))
+
+# Apply moving average smoothing
+smooth_y = np.array(raw_y)
+for _ in range(4):
+    smoothed = np.copy(smooth_y)
+    for j in range(1, len(smoothed) - 1):
+        smoothed[j] = (smooth_y[j - 1] + smooth_y[j] + smooth_y[j + 1]) / 3
+    smooth_y = smoothed
+smooth_x = raw_x
+
+# Refined style - publication-quality aesthetics
+custom_style = Style(
+    background="#FFFFFF",
+    plot_background="#FAFBFC",
+    foreground="#34495E",
+    foreground_strong="#2C3E50",
+    foreground_subtle="#E8ECF0",
+    colors=(
+        "#B0C4DE",  # non-significant: light steel blue (muted background)
+        "#E74C3C",  # upregulated: vibrant red
+        "#2980B9",  # downregulated: ocean blue
+        "#2C3E50",  # M=0 line: charcoal
+        "#7F8C8D",  # +1 threshold: warm gray
+        "#7F8C8D",  # -1 threshold: warm gray
+        "#E67E22",  # LOESS curve: bold orange
+        "#1A1A2E",  # top DE genes: near-black
+    ),
+    title_font_size=52,
+    label_font_size=40,
+    major_label_font_size=38,
+    legend_font_size=28,
+    value_font_size=22,
+    tooltip_font_size=26,
+    stroke_width=2,
+    opacity=0.6,
+    opacity_hover=0.95,
+)
+
+# Subsample for performance (pygal renders each point as SVG element)
+np.random.seed(42)
+sig_mask = significant
+nonsig_mask = ~significant
+
+sig_indices = np.where(sig_mask)[0]
+nonsig_indices = np.where(nonsig_mask)[0]
+n_nonsig_display = 1800
+nonsig_sample = np.random.choice(nonsig_indices, min(n_nonsig_display, len(nonsig_indices)), replace=False)
+
+# Split significant into up/down for visual storytelling
+sig_up_points = []
+sig_down_points = []
+nonsig_points = []
+
+for i in nonsig_sample:
+    nonsig_points.append(
+        {
+            "value": (round(float(mean_expression[i]), 2), round(float(log_fold_change[i]), 2)),
+            "label": f"{gene_names[i]} | A={mean_expression[i]:.1f}, M={log_fold_change[i]:.2f}, p={p_values[i]:.2e}",
+        }
+    )
+
+top_idx_set = set(top_idx.tolist())
+for i in sig_indices:
+    if i in top_idx_set:
+        continue
+    point = {
+        "value": (round(float(mean_expression[i]), 2), round(float(log_fold_change[i]), 2)),
+        "label": f"{gene_names[i]} | A={mean_expression[i]:.1f}, M={log_fold_change[i]:.2f}, p={p_values[i]:.2e}",
+    }
+    if log_fold_change[i] > 0:
+        sig_up_points.append(point)
+    else:
+        sig_down_points.append(point)
+
+# Top 10 most significant genes with star markers in tooltips
+labeled_points = []
+for i in top_idx:
+    labeled_points.append(
+        {
+            "value": (round(float(mean_expression[i]), 2), round(float(log_fold_change[i]), 2)),
+            "label": f"\u2605 {gene_names[i]} | A={mean_expression[i]:.1f}, M={log_fold_change[i]:.2f}, p={p_values[i]:.2e}",
+        }
+    )
+
+# Reference line endpoints
+x_min = 0
+x_max = float(np.percentile(mean_expression, 99.5))
+
+# Create XY chart
+chart = pygal.XY(
+    width=4800,
+    height=2700,
+    style=custom_style,
+    title="ma-differential-expression \u00b7 pygal \u00b7 pyplots.ai",
+    x_title="Mean Expression (A)",
+    y_title="Log\u2082 Fold Change (M)",
+    show_legend=True,
+    legend_at_bottom=True,
+    legend_at_bottom_columns=8,
+    legend_box_size=22,
+    dots_size=5,
+    stroke=False,
+    show_x_guides=False,
+    show_y_guides=False,
+    truncate_legend=-1,
+    print_values=False,
+    dynamic_print_values=True,
+    js=[],
+    x_label_rotation=0,
+    margin_bottom=80,
+)
+
+# Non-significant genes - small muted dots for background density
+chart.add("Not Significant", nonsig_points, dots_size=4)
+
+# Upregulated significant - vibrant red
+chart.add("Upregulated (p<0.05)", sig_up_points, dots_size=7)
+
+# Downregulated significant - ocean blue
+chart.add("Downregulated (p<0.05)", sig_down_points, dots_size=7)
+
+# M = 0 line (no change)
+chart.add("M = 0", [(x_min, 0), (x_max, 0)], stroke=True, show_dots=False, stroke_style={"width": 5})
+
+# M = +1 threshold (2-fold up)
+chart.add(
+    "+2-fold", [(x_min, 1), (x_max, 1)], stroke=True, show_dots=False, stroke_style={"width": 3, "dasharray": "14, 8"}
+)
+
+# M = -1 threshold (2-fold down)
+chart.add(
+    "\u22122-fold",
+    [(x_min, -1), (x_max, -1)],
+    stroke=True,
+    show_dots=False,
+    stroke_style={"width": 3, "dasharray": "14, 8"},
+)
+
+# LOESS smoothing curve - bold orange
+chart.add(
+    "LOESS trend",
+    [(round(x, 2), round(y, 3)) for x, y in zip(smooth_x, smooth_y, strict=False)],
+    stroke=True,
+    show_dots=True,
+    dots_size=5,
+    stroke_style={"width": 7},
+)
+
+# Top DE genes - large prominent dots
+chart.add("Top DE genes", labeled_points, dots_size=16, stroke=False)
+
+# Save
+chart.render_to_file("plot.html")
+chart.render_to_png("plot.png")