feat(seaborn): implement ma-differential-expression (#5085)

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

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

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

**Parent Issue:** #4420

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/23339077516)*

---------

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/seaborn.py

@@ -0,0 +1,183 @@
+""" pyplots.ai
+ma-differential-expression: MA Plot for Differential Expression
+Library: seaborn 0.13.2 | Python 3.14.3
+Quality: 94/100 | Created: 2026-03-20
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+# Seaborn theme and style
+sns.set_theme(style="ticks", context="talk", font_scale=1.1)
+palette = sns.color_palette("colorblind")
+TEAL = palette[0]  # colorblind-safe blue/teal
+GRAY = "#C0C0C0"
+ACCENT = palette[2]  # green for LOESS
+DARK = "#2C3E50"
+THRESHOLD_COLOR = palette[4]  # muted purple for threshold lines
+
+# Data - Simulated RNA-seq differential expression results (~15,000 genes)
+np.random.seed(42)
+n_genes = 15000
+
+mean_expression = np.random.exponential(scale=3, size=n_genes) + 1
+log_fold_change = np.random.normal(0, 0.5, n_genes)
+
+# Add truly differentially expressed genes (~8% of genes)
+n_de = int(n_genes * 0.08)
+de_indices = np.random.choice(n_genes, n_de, replace=False)
+log_fold_change[de_indices] += np.random.choice([-1, 1], n_de) * np.random.uniform(1.5, 4, n_de)
+
+# Simulate p-values: DE genes get small p-values, others get uniform
+p_values = np.ones(n_genes)
+p_values[de_indices] = 10 ** (-np.random.uniform(2, 10, n_de))
+p_values[~np.isin(np.arange(n_genes), de_indices)] = np.random.uniform(0.01, 1.0, n_genes - n_de)
+
+significant = p_values < 0.05
+
+# Categorize genes for seaborn hue mapping
+status = np.where(
+    ~significant,
+    "Not significant",
+    np.where(log_fold_change > 1, "Up-regulated", np.where(log_fold_change < -1, "Down-regulated", "Significant")),
+)
+
+# Gene names for top hits - spread across expression range for better storytelling
+gene_names = [None] * n_genes
+top_gene_labels = ["BRCA1", "TP53", "MYC", "EGFR", "VEGFA", "IL6"]
+sig_de_mask = significant & (np.abs(log_fold_change) > 1)
+sig_de_indices = np.where(sig_de_mask)[0]
+sig_de_expr = mean_expression[sig_de_indices]
+sig_de_abs_lfc = np.abs(log_fold_change[sig_de_indices])
+
+# Use evenly-spaced expression bins across full range to spread labels
+expr_min, expr_max = sig_de_expr.min(), sig_de_expr.max()
+n_labels = len(top_gene_labels)
+expr_edges = np.linspace(expr_min, expr_max + 0.01, n_labels + 1)
+top_sig = []
+for b in range(n_labels):
+    in_bin = (sig_de_expr >= expr_edges[b]) & (sig_de_expr < expr_edges[b + 1])
+    if not np.any(in_bin):
+        continue
+    bin_idx = np.where(in_bin)[0]
+    best = bin_idx[np.argmax(sig_de_abs_lfc[bin_idx])]
+    top_sig.append(sig_de_indices[best])
+
+for i, idx in enumerate(top_sig[:n_labels]):
+    gene_names[idx] = top_gene_labels[i]
+
+df = pd.DataFrame(
+    {
+        "Mean Expression (A)": mean_expression,
+        "Log₂ Fold Change (M)": log_fold_change,
+        "Status": pd.Categorical(
+            status, categories=["Not significant", "Significant", "Up-regulated", "Down-regulated"]
+        ),
+        "gene_name": gene_names,
+    }
+)
+
+# Color palette for categories - colorblind-safe
+status_palette = {
+    "Not significant": GRAY,
+    "Significant": sns.desaturate(TEAL, 0.6),
+    "Up-regulated": TEAL,
+    "Down-regulated": palette[3],  # red from colorblind palette
+}
+
+# Plot using seaborn's hue-based scatter
+fig, ax = plt.subplots(figsize=(16, 9))
+
+sns.scatterplot(
+    data=df,
+    x="Mean Expression (A)",
+    y="Log₂ Fold Change (M)",
+    hue="Status",
+    hue_order=["Not significant", "Significant", "Up-regulated", "Down-regulated"],
+    palette=status_palette,
+    size="Status",
+    sizes={"Not significant": 20, "Significant": 40, "Up-regulated": 55, "Down-regulated": 55},
+    alpha=0.3,
+    edgecolor="none",
+    legend="full",
+    ax=ax,
+)
+
+# Emphasize up/down-regulated with white edges via a second scatter layer
+de_data = df[df["Status"].isin(["Up-regulated", "Down-regulated"])]
+sns.scatterplot(
+    data=de_data,
+    x="Mean Expression (A)",
+    y="Log₂ Fold Change (M)",
+    hue="Status",
+    hue_order=["Up-regulated", "Down-regulated"],
+    palette={"Up-regulated": TEAL, "Down-regulated": palette[3]},
+    s=55,
+    alpha=0.6,
+    edgecolor="white",
+    linewidth=0.4,
+    legend=False,
+    ax=ax,
+)
+
+# Reference lines
+ax.axhline(y=0, color=DARK, linewidth=2, alpha=0.5)
+ax.axhline(y=1, color=THRESHOLD_COLOR, linewidth=1.5, linestyle="--", alpha=0.7)
+ax.axhline(y=-1, color=THRESHOLD_COLOR, linewidth=1.5, linestyle="--", alpha=0.7)
+ax.text(ax.get_xlim()[1] * 0.97, 1.12, "2-fold ↑", fontsize=12, color=THRESHOLD_COLOR, ha="right", fontstyle="italic")
+ax.text(ax.get_xlim()[1] * 0.97, -1.35, "2-fold ↓", fontsize=12, color=THRESHOLD_COLOR, ha="right", fontstyle="italic")
+
+# LOESS smoothing curve using seaborn regplot with lowess
+sns.regplot(
+    data=df,
+    x="Mean Expression (A)",
+    y="Log₂ Fold Change (M)",
+    lowess=True,
+    scatter=False,
+    line_kws={"color": ACCENT, "linewidth": 3, "alpha": 0.85, "label": "LOESS trend"},
+    ax=ax,
+)
+
+# Label top differentially expressed genes - spread across expression range
+labeled = df[df["gene_name"].notna()].copy()
+label_positions = []
+for _, row in labeled.iterrows():
+    x_val = row["Mean Expression (A)"]
+    y_val = row["Log₂ Fold Change (M)"]
+    # Place labels outward from the dense center: above for up-regulated, below for down
+    y_off = -30 if y_val > 0 else 30
+    x_off = 25 if x_val < df["Mean Expression (A)"].median() else -25
+    # Avoid overlapping previously placed labels
+    for px, py in label_positions:
+        if abs(x_val - px) < 2 and abs(y_val - py) < 1:
+            y_off = y_off + (35 if y_off > 0 else -35)
+            break
+    label_positions.append((x_val, y_val))
+    ax.annotate(
+        row["gene_name"],
+        xy=(x_val, y_val),
+        xytext=(x_off, y_off),
+        textcoords="offset points",
+        fontsize=13,
+        fontweight="bold",
+        color=DARK,
+        arrowprops={"arrowstyle": "->", "color": DARK, "lw": 1.2, "connectionstyle": "arc3,rad=0.2"},
+        bbox={"boxstyle": "round,pad=0.25", "facecolor": "white", "edgecolor": DARK, "alpha": 0.85, "linewidth": 0.8},
+    )
+
+# Style - use seaborn's despine
+sns.despine(ax=ax)
+ax.set_xlabel("Mean Expression (A)", fontsize=20)
+ax.set_ylabel("Log₂ Fold Change (M)", fontsize=20)
+ax.set_title("ma-differential-expression · seaborn · pyplots.ai", fontsize=24, fontweight="medium", pad=15)
+ax.tick_params(axis="both", labelsize=16)
+ax.yaxis.grid(True, alpha=0.12, linewidth=0.8, color="#888888")
+
+# Refine legend with seaborn
+sns.move_legend(ax, "upper right", fontsize=13, framealpha=0.92, title="Gene Status", title_fontsize=14)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")