Bio Data Visualization Heatmaps Clustering

Version Compatibility

Reference examples tested with: ggplot2 3.5+, matplotlib 3.8+, pandas 2.2+, scanpy 1.10+, scipy 1.12+, seaborn 0.13+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Heatmaps and Clustering

"Create a clustered heatmap" → Visualize expression matrices or distance matrices with hierarchical clustering dendrograms.

• R: pheatmap::pheatmap(mat), ComplexHeatmap::Heatmap(mat)
• Python: seaborn.clustermap(df), scanpy.pl.heatmap(adata)

pheatmap (R) - Quick Heatmaps

library(pheatmap)
library(RColorBrewer)

# Basic heatmap with clustering
pheatmap(mat, scale = 'row', cluster_rows = TRUE, cluster_cols = TRUE)

# With annotations
annotation_col <- data.frame(
    Condition = metadata$condition,
    Batch = metadata$batch,
    row.names = colnames(mat)
)

annotation_row <- data.frame(
    Pathway = gene_info$pathway,
    row.names = rownames(mat)
)

pheatmap(mat, scale = 'row',
         annotation_col = annotation_col,
         annotation_row = annotation_row,
         color = colorRampPalette(rev(brewer.pal(9, 'RdBu')))(100),
         show_rownames = FALSE,
         fontsize = 8)

pheatmap Customization

# Custom annotation colors
ann_colors <- list(
    Condition = c(Control = '#4DBBD5', Treatment = '#E64B35'),
    Batch = c(A = '#00A087', B = '#3C5488', C = '#F39B7F'),
    Pathway = c(Metabolism = '#8491B4', Signaling = '#91D1C2')
)

pheatmap(mat, scale = 'row',
         annotation_col = annotation_col,
         annotation_colors = ann_colors,
         clustering_distance_rows = 'correlation',
         clustering_distance_cols = 'euclidean',
         clustering_method = 'ward.D2',
         cutree_rows = 4,
         cutree_cols = 2,
         gaps_col = c(5, 10),
         border_color = NA,
         main = 'Gene Expression Heatmap')

ComplexHeatmap (R) - Advanced

library(ComplexHeatmap)
library(circlize)

# Color function
col_fun <- colorRamp2(c(-2, 0, 2), c('blue', 'white', 'red'))

# Basic heatmap
Heatmap(mat, name = 'Z-score', col = col_fun,
        cluster_rows = TRUE, cluster_columns = TRUE,
        show_row_names = FALSE, show_column_names = TRUE)

ComplexHeatmap with Annotations

Goal: Create a richly annotated heatmap with sample metadata, gene annotations, and split panels for grouped comparisons.

Approach: Define column and row HeatmapAnnotation objects with categorical colors and quantitative bar plots, then render with Heatmap using row_split and column_split for grouped display.

# Column annotation
ha_col <- HeatmapAnnotation(
    Condition = metadata$condition,
    Batch = metadata$batch,
    Age = anno_barplot(metadata$age),
    col = list(
        Condition = c(Control = '#4DBBD5', Treatment = '#E64B35'),
        Batch = c(A = '#00A087', B = '#3C5488')
    )
)

# Row annotation
ha_row <- rowAnnotation(
    Pathway = gene_info$pathway,
    LogFC = anno_barplot(gene_info$log2FC, baseline = 0,
                          gp = gpar(fill = ifelse(gene_info$log2FC > 0, 'red', 'blue'))),
    col = list(Pathway = c(Metabolism = '#8491B4', Signaling = '#91D1C2'))
)

Heatmap(mat, name = 'Z-score', col = col_fun,
        top_annotation = ha_col,
        left_annotation = ha_row,
        row_split = gene_info$pathway,
        column_split = metadata$condition)

Multiple Heatmaps

# Combine heatmaps horizontally
ht1 <- Heatmap(mat1, name = 'Expression', col = col_fun)
ht2 <- Heatmap(mat2, name = 'Methylation', col = colorRamp2(c(0, 0.5, 1), c('blue', 'white', 'red')))

ht_list <- ht1 + ht2
draw(ht_list, row_title = 'Genes', column_title = 'Samples')

seaborn (Python)

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Basic clustermap
g = sns.clustermap(df, cmap='RdBu_r', center=0, figsize=(10, 12),
                   row_cluster=True, col_cluster=True,
                   standard_scale=0)  # 0 = rows, 1 = columns
plt.savefig('heatmap.png', dpi=150, bbox_inches='tight')

seaborn with Annotations

# Create color mappings
condition_colors = {'Control': '#4DBBD5', 'Treatment': '#E64B35'}
batch_colors = {'A': '#00A087', 'B': '#3C5488', 'C': '#F39B7F'}

col_colors = pd.DataFrame({
    'Condition': metadata['condition'].map(condition_colors),
    'Batch': metadata['batch'].map(batch_colors)
})

row_colors = gene_info['pathway'].map({'Metabolism': '#8491B4', 'Signaling': '#91D1C2'})

g = sns.clustermap(df, cmap='RdBu_r', center=0,
                   row_colors=row_colors,
                   col_colors=col_colors,
                   figsize=(12, 14),
                   dendrogram_ratio=0.15,
                   cbar_pos=(0.02, 0.8, 0.03, 0.15))

g.ax_heatmap.set_xlabel('Samples')
g.ax_heatmap.set_ylabel('Genes')

Clustering Methods

# Distance metrics
# 'euclidean', 'correlation', 'manhattan', 'maximum', 'canberra', 'binary'

# Linkage methods
# 'complete', 'single', 'average', 'ward.D', 'ward.D2', 'mcquitty', 'median', 'centroid'

pheatmap(mat, clustering_distance_rows = 'correlation',
         clustering_distance_cols = 'euclidean',
         clustering_method = 'ward.D2')

Extract Cluster Assignments

# pheatmap
p <- pheatmap(mat, scale = 'row', cutree_rows = 4, silent = TRUE)
row_clusters <- cutree(p$tree_row, k = 4)

# ComplexHeatmap
ht <- Heatmap(mat, row_split = 4)
ht <- draw(ht)
row_order <- row_order(ht)

# seaborn
g = sns.clustermap(df, cmap='RdBu_r')
row_linkage = g.dendrogram_row.linkage
from scipy.cluster.hierarchy import fcluster
clusters = fcluster(row_linkage, t=4, criterion='maxclust')

Save Heatmaps

# pheatmap to file
pheatmap(mat, filename = 'heatmap.pdf', width = 8, height = 10)

# ComplexHeatmap to file
pdf('heatmap.pdf', width = 8, height = 10)
draw(ht)
dev.off()

Related Skills

• data-visualization/ggplot2-fundamentals - General plotting
• data-visualization/color-palettes - Color selection
• differential-expression/de-visualization - Expression heatmaps