A heatmap with hierarchical clustering dendrograms on rows and/or columns, showing both data values and their hierarchical relationships. Rows and columns are automatically reordered based on clustering results to reveal natural groupings in the data. Essential for discovering patterns in high-dimensional data where similar observations or variables should be visually grouped together.
""" anyplot.ai
heatmap-clustered: Clustered Heatmap
Library: plotly 6.7.0 | Python 3.13.13
Quality: 96/100 | Updated: 2026-05-09
"""
import os
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.spatial.distance import pdist
# Theme tokens
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"
DENDROGRAM_COLOR = "#306998" if THEME == "light" else "#6BA3D4"
PATHWAY_COLORS = ["#E8CCCC", "#CCE8CC", "#CCCCFF", "#FFCCCC"]
# Data: Gene expression analysis (20 genes x 12 samples)
np.random.seed(42)
n_genes = 20
n_samples = 12
# Gene names representing biological pathways
gene_labels = [
"CDK1",
"CCNB1",
"PLK1",
"AURKA",
"BUB1", # Cell cycle
"GAPDH",
"LDHA",
"PKM",
"HK2",
"ENO1", # Metabolism
"IL6",
"TNF",
"IFNG",
"IL1B",
"CXCL8", # Immune response
"MYC",
"TP53",
"BRCA1",
"EGFR",
"VEGFA", # Cancer-related
]
# Gene pathway annotations (0=cell cycle, 1=metabolism, 2=immune, 3=cancer)
gene_pathway = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3]
pathway_names = ["Cell Cycle", "Metabolism", "Immune", "Cancer"]
# Sample names (tumor vs normal comparisons)
sample_labels = [
"T1_A",
"T1_B",
"T1_C",
"T2_A",
"T2_B",
"T2_C", # Tumor
"N1_A",
"N1_B",
"N1_C",
"N2_A",
"N2_B",
"N2_C", # Normal
]
# Sample type annotations (0=tumor, 1=normal)
sample_type = [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
sample_type_names = ["Tumor", "Normal"]
sample_type_colors = ["#FFE8E8", "#E8E8FF"]
# Generate expression data with cluster structure
data = np.random.randn(n_genes, n_samples) * 0.5
# Cell cycle genes upregulated in tumors
data[0:5, 0:6] += 2.0
data[0:5, 6:12] -= 1.5
# Metabolism genes moderately upregulated in tumors
data[5:10, 0:6] += 1.2
data[5:10, 6:12] -= 0.8
# Immune genes show mixed pattern
data[10:15, 0:3] += 1.5
data[10:15, 3:6] -= 0.5
data[10:15, 6:9] += 0.8
data[10:15, 9:12] -= 1.2
# Cancer-related genes upregulated in tumors
data[15:20, 0:6] += 1.8
data[15:20, 6:12] -= 1.0
# Hierarchical clustering
row_linkage = linkage(pdist(data, metric="euclidean"), method="ward")
col_linkage = linkage(pdist(data.T, metric="euclidean"), method="ward")
# Get dendrogram order
row_dendro = dendrogram(row_linkage, no_plot=True)
col_dendro = dendrogram(col_linkage, no_plot=True)
row_order = row_dendro["leaves"]
col_order = col_dendro["leaves"]
# Reorder data and labels
data_ordered = data[row_order, :][:, col_order]
row_labels_ordered = [gene_labels[i] for i in row_order]
col_labels_ordered = [sample_labels[i] for i in col_order]
row_pathway_ordered = [gene_pathway[i] for i in row_order]
col_type_ordered = [sample_type[i] for i in col_order]
# Create subplots: top dendrogram, left dendrogram, main heatmap, colorbar space
fig = make_subplots(
rows=2,
cols=3,
column_widths=[0.08, 0.02, 0.90],
row_heights=[0.15, 0.85],
horizontal_spacing=0.003,
vertical_spacing=0.005,
specs=[[None, None, {}], [{}, {}, {}]],
)
# Add top dendrogram (column clustering)
col_icoord = np.array(col_dendro["icoord"])
col_dcoord = np.array(col_dendro["dcoord"])
for i in range(len(col_icoord)):
fig.add_trace(
go.Scatter(
x=col_icoord[i],
y=col_dcoord[i],
mode="lines",
line={"color": DENDROGRAM_COLOR, "width": 1.5},
showlegend=False,
hoverinfo="skip",
),
row=1,
col=3,
)
# Add left dendrogram (row clustering)
row_icoord = np.array(row_dendro["icoord"])
row_dcoord = np.array(row_dendro["dcoord"])
for i in range(len(row_icoord)):
fig.add_trace(
go.Scatter(
x=row_dcoord[i],
y=row_icoord[i],
mode="lines",
line={"color": DENDROGRAM_COLOR, "width": 1.5},
showlegend=False,
hoverinfo="skip",
),
row=2,
col=1,
)
# Add row pathway color bar
fig.add_trace(
go.Heatmap(
z=[[row_pathway_ordered]],
x=["Pathway"],
y=row_labels_ordered,
colorscale=[(i / 3, PATHWAY_COLORS[i]) for i in range(4)],
showscale=False,
hoverinfo="skip",
),
row=2,
col=2,
)
# Add heatmap
fig.add_trace(
go.Heatmap(
z=data_ordered,
x=col_labels_ordered,
y=row_labels_ordered,
colorscale="RdBu_r",
zmid=0,
colorbar={
"title": {"text": "Expression<br>(z-score)", "font": {"size": 20, "color": INK}},
"tickfont": {"size": 16, "color": INK_SOFT},
"len": 0.75,
"thickness": 25,
"x": 1.02,
"bgcolor": PAGE_BG,
},
hovertemplate="%{y}<br>%{x}<br>Value: %{z:.2f}<extra></extra>",
),
row=2,
col=3,
)
# Update axes for top dendrogram
fig.update_xaxes(
showticklabels=False,
showgrid=False,
zeroline=False,
showline=False,
range=[0, max(col_dendro["icoord"][-1])],
row=1,
col=3,
)
fig.update_yaxes(
showticklabels=False,
showgrid=False,
zeroline=False,
showline=False,
range=[0, max(col_dendro["dcoord"][-1]) * 1.05],
row=1,
col=3,
)
# Update axes for left dendrogram
fig.update_xaxes(
showticklabels=False,
showgrid=False,
zeroline=False,
showline=False,
range=[max(row_dendro["dcoord"][-1]) * 1.05, 0],
row=2,
col=1,
)
fig.update_yaxes(
showticklabels=False,
showgrid=False,
zeroline=False,
showline=False,
range=[0, max(row_dendro["icoord"][-1])],
row=2,
col=1,
)
# Update axes for row pathway color bar
fig.update_xaxes(showticklabels=False, showgrid=False, zeroline=False, showline=False, row=2, col=2)
fig.update_yaxes(showticklabels=False, showgrid=False, zeroline=False, showline=False, row=2, col=2)
# Update heatmap axes with labels
fig.update_xaxes(
title={"text": "Samples", "font": {"size": 22, "color": INK}},
tickfont={"size": 16, "color": INK_SOFT},
tickangle=45,
side="bottom",
row=2,
col=3,
)
fig.update_yaxes(
title={"text": "Genes", "font": {"size": 22, "color": INK}},
tickfont={"size": 16, "color": INK_SOFT},
row=2,
col=3,
)
# Update layout
fig.update_layout(
title={
"text": "heatmap-clustered · plotly · anyplot.ai",
"font": {"size": 28, "color": INK},
"x": 0.5,
"xanchor": "center",
},
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
font={"color": INK, "family": "sans-serif"},
showlegend=False,
margin={"l": 150, "r": 120, "t": 120, "b": 120},
hovermode="closest",
)
# Save outputs
fig.write_image(f"plot-{THEME}.png", width=1600, height=900, scale=3)
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")
Part of Clustered Heatmap on anyplot.ai.