feat(plotly): implement ma-differential-expression (#5079)

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

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

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

**Parent Issue:** #4420

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/23339077617)*

---------

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

Author: github-actions[bot]

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

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+""" pyplots.ai
+ma-differential-expression: MA Plot for Differential Expression
+Library: plotly 6.6.0 | Python 3.14.3
+Quality: 90/100 | Created: 2026-03-20
+"""
+
+import numpy as np
+import plotly.graph_objects as go
+from scipy.signal import savgol_filter
+
+
+# Data - Simulated RNA-seq differential expression results
+np.random.seed(42)
+n_genes = 15000
+
+# Mean expression (A values) - log2 scale, bimodal distribution
+mean_expression = np.concatenate([np.random.normal(4, 1.5, 5000), np.random.normal(9, 2.5, 10000)])
+mean_expression = np.clip(mean_expression, 0.5, 16)
+
+# Log fold change (M values) - most genes near zero, some DE genes
+log_fold_change = np.random.normal(0, 0.3, n_genes)
+
+# Add truly differentially expressed genes (~8%)
+n_de = 1200
+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.exponential(0.8, n_de) + 1.0)
+
+# Expression-dependent variance (higher variance at low expression)
+noise_scale = 0.5 / (1 + mean_expression * 0.3)
+log_fold_change += np.random.normal(0, noise_scale)
+
+# Significance (adjusted p-value < 0.05)
+significant = np.abs(log_fold_change) > 1.0
+significant &= mean_expression > 2.0
+significant[de_indices] = np.abs(log_fold_change[de_indices]) > 0.8
+
+# Gene names for top DE genes
+gene_names = [f"Gene{i}" for i in range(n_genes)]
+top_gene_names = ["BRCA1", "TP53", "MYC", "EGFR", "VEGFA", "IL6", "TNF", "STAT3", "KRAS", "CDK2"]
+top_de = np.argsort(np.abs(log_fold_change[significant]))[-10:]
+sig_indices = np.where(significant)[0]
+for i, name in zip(top_de, top_gene_names, strict=False):
+    gene_names[sig_indices[i]] = name
+
+# Separate data
+non_sig_mask = ~significant
+sig_mask = significant
+
+# LOESS-like smoothing curve
+sort_idx = np.argsort(mean_expression)
+sorted_expr = mean_expression[sort_idx]
+sorted_lfc = log_fold_change[sort_idx]
+window = min(501, len(sorted_lfc) // 4 * 2 + 1)
+smoothed = savgol_filter(sorted_lfc, window, 3)
+
+# Color palette - refined, distinctive
+COLOR_NONSIG = "#B0B8C4"
+COLOR_SIG = "#0E8A7A"
+COLOR_LOESS = "#306998"
+COLOR_REFLINE = "#5A5A6E"
+COLOR_THRESHOLD = "#9E9EAE"
+COLOR_LABEL = "#2B2B3D"
+
+# Plot
+fig = go.Figure()
+
+# Non-significant genes
+fig.add_trace(
+    go.Scatter(
+        x=mean_expression[non_sig_mask],
+        y=log_fold_change[non_sig_mask],
+        mode="markers",
+        marker={"size": 8, "color": COLOR_NONSIG, "opacity": 0.25, "line": {"width": 0}},
+        name="Not significant",
+        hovertemplate="A: %{x:.1f}<br>M: %{y:.2f}<extra>Not significant</extra>",
+    )
+)
+
+# Significant genes
+fig.add_trace(
+    go.Scatter(
+        x=mean_expression[sig_mask],
+        y=log_fold_change[sig_mask],
+        mode="markers",
+        marker={"size": 10, "color": COLOR_SIG, "opacity": 0.6, "line": {"width": 0.5, "color": "white"}},
+        name="Significant (padj < 0.05)",
+        hovertemplate="A: %{x:.1f}<br>M: %{y:.2f}<extra>Significant</extra>",
+    )
+)
+
+# Smoothing curve
+fig.add_trace(
+    go.Scatter(
+        x=sorted_expr,
+        y=smoothed,
+        mode="lines",
+        line={"color": COLOR_LOESS, "width": 3.5},
+        name="LOESS trend",
+        hoverinfo="skip",
+    )
+)
+
+# Reference line at y=0 (distinct from LOESS)
+fig.add_hline(y=0, line={"color": COLOR_REFLINE, "width": 1.5, "dash": "dot"})
+
+# Fold-change threshold lines with annotations
+fig.add_hline(y=1, line={"color": COLOR_THRESHOLD, "width": 1.5, "dash": "dash"})
+fig.add_hline(y=-1, line={"color": COLOR_THRESHOLD, "width": 1.5, "dash": "dash"})
+
+fig.add_annotation(
+    x=15.5,
+    y=1,
+    text="2-fold up",
+    showarrow=False,
+    font={"size": 14, "color": COLOR_THRESHOLD},
+    xanchor="right",
+    yshift=14,
+)
+fig.add_annotation(
+    x=15.5,
+    y=-1,
+    text="2-fold down",
+    showarrow=False,
+    font={"size": 14, "color": COLOR_THRESHOLD},
+    xanchor="right",
+    yshift=-14,
+)
+
+# Label top DE genes
+label_indices = [sig_indices[i] for i in top_de]
+fig.add_trace(
+    go.Scatter(
+        x=mean_expression[label_indices],
+        y=log_fold_change[label_indices],
+        mode="text",
+        text=[gene_names[i] for i in label_indices],
+        textposition="top center",
+        textfont={"size": 16, "color": COLOR_LABEL, "family": "Arial Black, sans-serif"},
+        showlegend=False,
+        hoverinfo="skip",
+    )
+)
+
+# Layout
+fig.update_layout(
+    title={
+        "text": "ma-differential-expression · plotly · pyplots.ai",
+        "font": {"size": 28, "color": "#2B2B3D", "family": "Arial, sans-serif"},
+        "x": 0.5,
+        "xanchor": "center",
+    },
+    xaxis={
+        "title": {"text": "Mean Expression (A)", "font": {"size": 22, "color": "#3A3A4A"}},
+        "tickfont": {"size": 18, "color": "#4A4A5A"},
+        "showgrid": False,
+        "zeroline": False,
+    },
+    yaxis={
+        "title": {"text": "Log₂ Fold Change (M)", "font": {"size": 22, "color": "#3A3A4A"}},
+        "tickfont": {"size": 18, "color": "#4A4A5A"},
+        "gridcolor": "rgba(0,0,0,0.06)",
+        "showgrid": True,
+        "zeroline": False,
+    },
+    template="plotly_white",
+    legend={
+        "font": {"size": 16, "color": "#3A3A4A"},
+        "x": 0.98,
+        "y": 0.98,
+        "xanchor": "right",
+        "bgcolor": "rgba(255,255,255,0.85)",
+        "bordercolor": "rgba(0,0,0,0.1)",
+        "borderwidth": 1,
+    },
+    margin={"l": 100, "r": 80, "t": 100, "b": 100},
+    plot_bgcolor="white",
+)
+
+# Save
+fig.write_image("plot.png", width=1600, height=900, scale=3)
+fig.write_html("plot.html", include_plotlyjs="cdn")