An MA plot (M-versus-A plot) visualizes the relationship between log fold change (M) and mean average expression (A) when comparing two experimental conditions. Each point represents a gene or feature, with significantly differentially expressed genes highlighted. This plot is a standard diagnostic tool in RNA-seq and microarray analysis for assessing differential expression results and detecting systematic expression-dependent bias.
""" anyplot.ai
ma-differential-expression: MA Plot for Differential Expression
Library: plotly 6.8.0 | Python 3.13.14
Quality: 82/100 | Updated: 2026-06-21
"""
import os
import numpy as np
import plotly.graph_objects as go
from scipy.signal import savgol_filter
# Theme tokens — Imprint palette, theme-adaptive chrome
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
GRID = "rgba(26,26,23,0.15)" if THEME == "light" else "rgba(240,239,232,0.15)"
# Imprint palette assignments
COLOR_SIG = "#009E73" # Imprint position 1 — significant genes (first series)
COLOR_LOESS = "#4467A3" # Imprint position 3 — LOESS trend line
COLOR_THRESHOLD = "#DDCC77" # Imprint amber — fold-change threshold markers
# Data — simulated RNA-seq differential expression results
np.random.seed(42)
n_genes = 15000
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 = np.random.normal(0, 0.3, n_genes)
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)
noise_scale = 0.5 / (1 + mean_expression * 0.3)
log_fold_change += np.random.normal(0, noise_scale)
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 = [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
non_sig_mask = ~significant
sig_mask = significant
# LOESS-like smoothing curve via Savitzky-Golay filter
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)
# Plot
fig = go.Figure()
# Non-significant genes — muted, recede into background
fig.add_trace(
go.Scatter(
x=mean_expression[non_sig_mask],
y=log_fold_change[non_sig_mask],
mode="markers",
marker={"size": 5, "color": INK_MUTED, "opacity": 0.3, "line": {"width": 0}},
name="Not significant",
hovertemplate="A: %{x:.1f}<br>M: %{y:.2f}<extra>Not significant</extra>",
)
)
# Significant genes — Imprint brand green (first series)
fig.add_trace(
go.Scatter(
x=mean_expression[sig_mask],
y=log_fold_change[sig_mask],
mode="markers",
marker={"size": 8, "color": COLOR_SIG, "opacity": 0.65, "line": {"width": 0.5, "color": PAGE_BG}},
name="Significant (padj < 0.05)",
hovertemplate="A: %{x:.1f}<br>M: %{y:.2f}<extra>Significant</extra>",
)
)
# LOESS smoothing curve — Imprint blue
fig.add_trace(
go.Scatter(
x=sorted_expr,
y=smoothed,
mode="lines",
line={"color": COLOR_LOESS, "width": 3},
name="LOESS trend",
hoverinfo="skip",
)
)
# Reference line at M = 0 (no fold change)
fig.add_hline(y=0, line={"color": INK_SOFT, "width": 1.5, "dash": "dot"})
# Fold-change threshold lines (amber = caution/threshold role)
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"})
# Threshold labels — right-justified on the threshold lines, inside plot
fig.add_annotation(
x=15.2,
y=1,
text="2-fold up",
showarrow=False,
font={"size": 12, "color": COLOR_THRESHOLD},
xanchor="right",
yanchor="bottom",
yshift=4,
bgcolor=ELEVATED_BG,
borderpad=2,
)
fig.add_annotation(
x=15.2,
y=-1,
text="2-fold down",
showarrow=False,
font={"size": 12, "color": COLOR_THRESHOLD},
xanchor="right",
yanchor="top",
yshift=-4,
bgcolor=ELEVATED_BG,
borderpad=2,
)
# Label top DE genes with arrow connectors to avoid overlap with data points
label_indices = [sig_indices[i] for i in top_de]
expr_mid = (mean_expression.max() + mean_expression.min()) / 2
for i, gene_idx in enumerate(label_indices):
lfc = log_fold_change[gene_idx]
expr = mean_expression[gene_idx]
# Right-side genes point left; left-side genes alternate direction to reduce crowding
ax = -32 if expr > expr_mid else (28 if i % 2 == 0 else -28)
ay = -35 if lfc > 0 else 35
fig.add_annotation(
x=expr,
y=lfc,
text=gene_names[gene_idx],
showarrow=True,
arrowhead=2,
arrowsize=0.8,
arrowwidth=1.2,
arrowcolor=INK_SOFT,
ax=ax,
ay=ay,
font={"size": 10, "color": INK},
bgcolor=ELEVATED_BG,
bordercolor=INK_SOFT,
borderwidth=0.5,
borderpad=3,
opacity=0.9,
)
title_text = "ma-differential-expression · python · plotly · anyplot.ai"
title_fontsize = max(11, round(16 * min(1.0, 67 / len(title_text))))
fig.update_layout(
autosize=False,
title={
"text": title_text,
"font": {"size": title_fontsize, "color": INK, "family": "Arial, sans-serif"},
"x": 0.5,
"xanchor": "center",
},
xaxis={
"title": {"text": "Mean Expression (A)", "font": {"size": 12, "color": INK}},
"tickfont": {"size": 10, "color": INK_SOFT},
"showgrid": False,
"zeroline": False,
"showline": False,
},
yaxis={
"title": {"text": "Log₂ Fold Change (M)", "font": {"size": 12, "color": INK}},
"tickfont": {"size": 10, "color": INK_SOFT},
"gridcolor": GRID,
"showgrid": True,
"zeroline": False,
"linecolor": INK_SOFT,
},
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
font={"color": INK},
legend={
"font": {"size": 10, "color": INK_SOFT},
"x": 0.86,
"y": 0.98,
"xanchor": "right",
"bgcolor": ELEVATED_BG,
"bordercolor": INK_SOFT,
"borderwidth": 1,
},
margin={"l": 80, "r": 40, "t": 80, "b": 60},
)
# Save — canonical 3200×1800 landscape (width=800, height=450, scale=4)
fig.write_image(f"plot-{THEME}.png", width=800, height=450, scale=4)
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")
Part of MA Plot for Differential Expression on anyplot.ai.