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: letsplot 4.10.1 | Python 3.13.14
Quality: 91/100 | Updated: 2026-06-21
"""
import os
import numpy as np
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
coord_cartesian,
element_blank,
element_line,
element_rect,
element_text,
geom_hline,
geom_label,
geom_point,
geom_segment,
geom_smooth,
ggplot,
ggsize,
labs,
layer_tooltips,
scale_color_manual,
theme,
theme_minimal,
)
from lets_plot.export import ggsave
LetsPlot.setup_html()
# Theme tokens (see prompts/default-style-guide.md "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"
# Imprint palette — semantic: green = gain/up-regulated, red = loss/down-regulated
UP_COLOR = "#009E73" # Imprint position 1: brand green (growth/gain)
DOWN_COLOR = "#AE3030" # Imprint position 5: matte red (loss/error)
ANYPLOT_AMBER = "#DDCC77" # caution anchor — LOESS bias indicator
# Data
np.random.seed(42)
n_genes = 15000
mean_expression = np.random.uniform(0.5, 15, n_genes)
# Expression-dependent variance creates funnel shape (wider spread at low expression)
log_fold_change = np.random.normal(0, 0.3, n_genes)
low_expr_bias = 0.4 * np.exp(-0.3 * mean_expression)
log_fold_change += np.random.normal(0, low_expr_bias)
# Differentially expressed genes — only among moderately expressed genes
n_up, n_down = 420, 380
up_idx = np.random.choice(np.where(mean_expression > 2)[0], n_up, replace=False)
down_idx = np.random.choice(np.setdiff1d(np.where(mean_expression > 2)[0], up_idx), n_down, replace=False)
log_fold_change[up_idx] = np.random.uniform(1.2, 5.5, n_up)
log_fold_change[down_idx] = np.random.uniform(-5.5, -1.2, n_down)
p_values = np.ones(n_genes)
p_values[up_idx] = np.random.uniform(1e-20, 0.01, n_up)
p_values[down_idx] = np.random.uniform(1e-20, 0.01, n_down)
significant = p_values < 0.05
status = np.where(~significant, "Not significant", np.where(log_fold_change > 0, "Up-regulated", "Down-regulated"))
df = pd.DataFrame(
{
"A": mean_expression,
"M": log_fold_change,
"status": pd.Categorical(status, categories=["Down-regulated", "Not significant", "Up-regulated"]),
}
)
df_nonsig = df[df["status"] == "Not significant"]
df_sig = df[df["status"] != "Not significant"]
# Top gene labels — real cancer biology gene names
real_names_up = ["FOXM1", "CDK1", "MYC", "EGFR"]
real_names_down = ["CDKN1A", "RB1", "BRCA2", "TP53"]
top_up = up_idx[np.argsort(log_fold_change[up_idx])[-4:]]
top_down = down_idx[np.argsort(log_fold_change[down_idx])[:4]]
top_idx = np.concatenate([top_up, top_down])
nudges = [(-1.2, 0.6), (0.8, 0.9), (-1.0, 1.4), (1.2, 0.5), (0.9, -0.6), (-1.1, -0.9), (-0.6, -1.4), (1.0, -0.5)]
df_labels = pd.DataFrame(
{
"A": mean_expression[top_idx],
"M": log_fold_change[top_idx],
"gene": real_names_up + real_names_down,
"label_x": mean_expression[top_idx] + [n[0] for n in nudges],
"label_y": log_fold_change[top_idx] + [n[1] for n in nudges],
"regulation": ["Up-regulated"] * 4 + ["Down-regulated"] * 4,
}
)
# Plot — ggplot(df) as base for idiomatic single-data approach
plot = (
ggplot(df, aes(x="A", y="M"))
+ geom_hline(yintercept=0, color=INK, size=0.8)
+ geom_hline(yintercept=1, color=INK_SOFT, size=0.5, linetype="dashed")
+ geom_hline(yintercept=-1, color=INK_SOFT, size=0.5, linetype="dashed")
+ geom_point(data=df_nonsig, color=INK_MUTED, size=1.0, alpha=0.20)
+ geom_point(
aes(color="status"),
data=df_sig,
size=2.5,
alpha=0.70,
tooltips=layer_tooltips()
.line("@status")
.line("Mean expr: @A")
.line("Log₂FC: @M")
.format("A", ".1f")
.format("M", ".2f"),
)
+ geom_smooth(color=ANYPLOT_AMBER, size=1.5, se=False, method="loess", span=0.3)
+ geom_segment(
aes(x="A", y="M", xend="label_x", yend="label_y"), data=df_labels, color=INK_SOFT, size=0.4, linetype="dotted"
)
+ geom_label(
aes(x="label_x", y="label_y", label="gene", color="regulation"),
data=df_labels,
size=4,
fill=ELEVATED_BG,
alpha=0.90,
label_padding=0.3,
label_r=0.2,
label_size=0.5,
show_legend=False,
)
+ scale_color_manual(values={"Up-regulated": UP_COLOR, "Down-regulated": DOWN_COLOR}, name="Regulation")
+ labs(
x="Mean Expression (A)",
y="Log₂ Fold Change (M)",
title="ma-differential-expression · python · letsplot · anyplot.ai",
)
+ coord_cartesian(xlim=[0, 16])
+ ggsize(800, 450)
+ theme_minimal()
+ theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
panel_grid_major=element_line(color=INK_SOFT, size=0.2),
panel_grid_major_x=element_blank(),
panel_grid_minor=element_blank(),
axis_title=element_text(color=INK, size=12),
axis_text=element_text(color=INK_SOFT, size=10),
axis_line=element_line(color=INK_SOFT),
plot_title=element_text(color=INK, size=16),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_text=element_text(color=INK_SOFT, size=10),
legend_title=element_text(color=INK, size=10),
legend_position="bottom",
plot_margin=[30, 20, 10, 20],
)
)
# Save
ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of MA Plot for Differential Expression on anyplot.ai.