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: matplotlib 3.11.0 | Python 3.13.14
Quality: 90/100 | Updated: 2026-06-21
"""
import os
import sys
# Remove the script's own directory from sys.path so that this file (matplotlib.py)
# doesn't shadow the installed matplotlib package when run from its own directory.
_here = os.path.dirname(os.path.abspath(__file__))
sys.path = [p for p in sys.path if not p or os.path.abspath(p) != _here]
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import UnivariateSpline
# Theme tokens — Imprint palette (see default-style-guide.md)
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"
# Imprint palette — semantic assignment for directional expression
UP_COLOR = "#009E73" # position 1, brand green — upregulated (gain)
DOWN_COLOR = "#AE3030" # position 5, matte red — downregulated (loss, semantic exception)
TREND_COLOR = "#4467A3" # position 3, blue — LOESS trend line
# Data
np.random.seed(42)
n_genes = 15000
mean_expression = np.random.exponential(scale=3.0, size=n_genes) + 0.5
log_fold_change = np.random.normal(0, 0.4, size=n_genes)
n_up = 400
n_down = 350
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(-2.5, 0.8, n_down)
sig_mask = np.zeros(n_genes, dtype=bool)
sig_mask[up_idx] = True
sig_mask[down_idx] = True
nonsig_mask = ~sig_mask
# Top genes for labeling — alternating offsets to reduce crowding
top_up_idx = up_idx[np.argsort(log_fold_change[up_idx])[-4:]]
top_down_idx = down_idx[np.argsort(log_fold_change[down_idx])[:4]]
label_idx = np.concatenate([top_up_idx, top_down_idx])
label_names = ["BRCA1", "TP53", "MYC", "EGFR", "PTEN", "RB1", "APC", "KRAS"]
label_offsets = [
(30, 18),
(-42, 15),
(30, -18),
(-42, -15), # upregulated: alternate right/left
(30, -18),
(-42, -15),
(30, 18),
(-42, 15), # downregulated: alternate right/left
]
# Plot — canvas: figsize=(8, 4.5) × dpi=400 → 3200×1800 px (hard contract)
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Non-significant background cloud
ax.scatter(
mean_expression[nonsig_mask],
log_fold_change[nonsig_mask],
s=7,
alpha=0.10,
color=INK_MUTED,
edgecolors="none",
rasterized=True,
label="Not significant",
zorder=2,
)
# Downregulated genes
ax.scatter(
mean_expression[down_idx],
log_fold_change[down_idx],
s=22,
alpha=0.55,
color=DOWN_COLOR,
edgecolors=PAGE_BG,
linewidth=0.3,
rasterized=True,
label=f"Downregulated (n={n_down})",
zorder=3,
)
# Upregulated genes
ax.scatter(
mean_expression[up_idx],
log_fold_change[up_idx],
s=22,
alpha=0.55,
color=UP_COLOR,
edgecolors=PAGE_BG,
linewidth=0.3,
rasterized=True,
label=f"Upregulated (n={n_up})",
zorder=3,
)
# Reference lines
ax.axhline(y=0, color=INK, linewidth=1.2, alpha=0.85, zorder=1)
ax.axhline(y=1, color=INK_SOFT, linewidth=0.8, linestyle="--", alpha=0.45, zorder=1)
ax.axhline(y=-1, color=INK_SOFT, linewidth=0.8, linestyle="--", alpha=0.45, zorder=1)
# LOESS trend — binned spline approximation over all genes
sorted_idx = np.argsort(mean_expression)
x_sorted = mean_expression[sorted_idx]
y_sorted = log_fold_change[sorted_idx]
bin_count = 80
bin_edges = np.linspace(x_sorted.min(), np.percentile(x_sorted, 98), bin_count + 1)
bin_centers, bin_means = [], []
for i in range(bin_count):
in_bin = (x_sorted >= bin_edges[i]) & (x_sorted < bin_edges[i + 1])
if in_bin.sum() > 10:
bin_centers.append((bin_edges[i] + bin_edges[i + 1]) / 2)
bin_means.append(np.mean(y_sorted[in_bin]))
bin_centers = np.array(bin_centers)
bin_means = np.array(bin_means)
spline = UnivariateSpline(bin_centers, bin_means, s=len(bin_centers) * 0.5)
x_smooth = np.linspace(bin_centers.min(), bin_centers.max(), 200)
y_smooth = spline(x_smooth)
ax.plot(x_smooth, y_smooth, color=TREND_COLOR, linewidth=2.5, alpha=0.85, label="LOESS trend", zorder=4)
# Gene annotations with connector arrows (arrowprops) and background bbox for readability
for gene_idx, name, (dx, dy) in zip(label_idx, label_names, label_offsets, strict=False):
ax.annotate(
name,
xy=(mean_expression[gene_idx], log_fold_change[gene_idx]),
xytext=(dx, dy),
textcoords="offset points",
fontsize=7,
fontweight="bold",
fontstyle="italic",
color=INK,
bbox={"facecolor": ELEVATED_BG, "edgecolor": "none", "alpha": 0.85, "pad": 1.5, "boxstyle": "round,pad=0.2"},
arrowprops={"arrowstyle": "-", "color": INK_SOFT, "linewidth": 0.7, "shrinkA": 3, "shrinkB": 3},
zorder=5,
)
# Style — font sizes per library guide (3200×1800 canvas)
title = "ma-differential-expression · python · matplotlib · anyplot.ai"
ax.set_xlabel("Mean Expression (A)", fontsize=10, color=INK)
ax.set_ylabel("Log₂ Fold Change (M)", fontsize=10, color=INK)
ax.set_title(title, fontsize=12, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=8, colors=INK_SOFT)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_color(INK_SOFT)
ax.spines["bottom"].set_color(INK_SOFT)
ax.yaxis.grid(True, alpha=0.15, linewidth=0.6, color=INK)
leg = ax.legend(fontsize=7.5, loc="upper right", framealpha=0.9, edgecolor=INK_SOFT, fancybox=False)
if leg:
leg.get_frame().set_facecolor(ELEVATED_BG)
plt.setp(leg.get_texts(), color=INK_SOFT)
fig.subplots_adjust(left=0.09, right=0.97, top=0.93, bottom=0.11)
# Save — bbox_inches must stay default (None) to preserve 3200×1800 canvas
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)
plt.close()
Part of MA Plot for Differential Expression on anyplot.ai.