Create a clustered and annotated heatmaps using R pheatmap package (detailed guide)

Renesh Bedre    5 minute read

heatmaps with pheatmap R package

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In bioinformatics, heatmaps are commonly used to visualize gene expression changes across multiple genes and conditions. This article describes how to create clustered and annotated heatmaps for visualization of gene expression data obtained from RNA-seq experiments using a pheatmap R package.

Install pheatmap

If you have not installed pheatmap package, you can install it using install.packages() function.

# install pheatmap

Load pheatmap library

You need to load the pheatmap library for creating the heatmaps. I am using the RStudio console with R version 4.2.1

# load pheatmap (v1.0.12)

Load dataset

For this pheatmap tuotrial, I will use the gene expressiion dataset generated from RNA-seq experiment in cotton plant in response to pathogenic infection. For simplicity of understanding, I have put the conditions names as A to F. You can import the CSV dataset using read.csv() base R function. You can read my article on different ways to import CSV datasets in R.

# load dataset
df = read.csv("", row.names="Gene")
# convert to matrix
df_mat = data.matrix(df)
# view first 5 genes of data
head(df, 5)
# output
             A         B         C        D        E         F
B-CHI1 4.505700  3.260360 -1.249400  8.89807  8.05955 -0.842803
CTL2-1 3.508560  1.660790 -1.856680 -2.57336 -1.37370  1.196000
B-CHI2 2.160030  3.146520  0.982809  9.02430  6.05832 -2.967420
CTL2-2 1.884750  2.295690  0.408891 -3.91404 -2.28049  1.628820
CHIV1  0.255193 -0.761204 -1.022350  3.65059  2.46525 -1.188140

Tip: While importing a dataset, you should always make gene names columns as row names. Otherwise, you would encounter an error while creating a heatmap with pheatmap.

Create heatmap

Basic heatmap

You can create a basic clustered heatmap using the pheatmap. You need to pass the data matrix to the pheatmap() function.

pheatmap(df_mat, main = "basic heatmap")

heatmpap with pheatmap

Scale heatmap

pheatmap provides a parameter scale to rescale the default values in column or row direction. The rescaling is useful when the values in the dataset are very different from each other. For example, some gene expression values are very high and some are very low.

# row scaling
pheatmap(df_mat, scale = "row", main = "row scaling")

# column scaling
pheatmap(df_mat, scale = "column", main = "column scaling")

pheatmap scaling
with row pheatmap scaling
with column

Row and column clustering

By default, pheatmap applies hierarchical clustering to both rows and columns. You can choose if you want to display row/columns clustering or not by using cluster_rows and cluster_cols options.

# No row clustering
pheatmap(df_mat, cluster_rows = FALSE, main = "no row clustering")

# No column clustering
pheatmap(df_mat, cluster_cols = FALSE, main = "no column clustering")

pheatmap row
clustering pheatmap column

Color palette

You can change the color palette of the heatmap using the colorRampPalette() function, which returns a fixed number of colors.

# green and red color palette
color <- colorRampPalette((c("red", "black", "green")))(50)
pheatmap(df_mat, color = color, main = "red-green color palette")

# blue and green color palette
color <- colorRampPalette((c("blue", "black", "red")))(50)
pheatmap(df_mat, color = color, main = "blue-red color palette")

with red green color pheatmap 
with blue red color

Add annotations

You can add annotations to the row and columns to enhance the informative visualization for the heatmap. For example, you can add an annotation to a group of genes involved in the same biological pathway or you can add an annotation to the samples based on their conditions.

Here, I will add an annotation to genes (row) and samples (column) based on their gene functions and conditions.

# create a data frame for column annotation
smaple_group <- data.frame(sample = rep(c("infected", "control"), c(3, 3)))
row.names(smaple_group) <- colnames(df_mat)
# view data frame
A infected
B infected
C infected
D  control
E  control
F  control

# create a data frame for row annotation
gene_functions = read.csv("", row.names="Gene")
# view data frame
B-CHI1 antifungal 
CTL2-1 antifungal 
B-CHI2 antifungal 
CTL2-2 antifungal 
CHIV1  antifungal 
CHIA   antifungal 

# heatmap with annotations
pheatmap(df_mat, annotation_row = gene_functions, annotation_col = smaple_group, 
         main = "heatmap with annotations")

with annotations

Split heatmap clusters

pheatmap provides parameters cutree_rows and cutree_cols to split the rows and columns based on the number of cluster. If you can visually see distinct clusters, you can provided that numbers to these paramaters. For example, for columns I can see three distinct clusters.

# heatmap with cluster break
pheatmap(df_mat, cutree_rows = 5, cutree_cols = 3, main = "heatmap with clusterized output")

with split clusters

Get the heatmap data belonging to each row cluster,

# get data that belong to row clusters
hm <- pheatmap(df_mat)
df_row_cluster = data.frame(cluster = cutree(hm$tree_row, k = 5))
# view the genes clsuters
B-CHI1       1
CTL2-1       2
B-CHI2       1
CTL2-2       2
CHIV1        1
CHIA         1

Get the heatmap data belonging to each column cluster,

# get data that belong to column clusters
hm <- pheatmap(df_mat)
df_col_cluster = data.frame(cluster = cutree(hm$tree_col, k = 3))
# view the genes clsuters
A       1
B       1
C       2
D       3
E       3
F       2

Add gaps in the heatmap

In pheatmap, you can add gaps in heatmap between specific columns or rows to separate the heatmap. For example, you can add gaps to differentiate the heatmaps by the functional significance of the genes.

# add gap between 2-3 and 4-5 columns
pheatmap(df_mat, gaps_col = c(2, 4), gaps_row = c(5), cluster_cols = FALSE, cluster_row = FALSE, 
         border_color = FALSE, main = "heatmap with row and column gap")

with row and column gaps

Tip: If you would like to enable gaps in heatmaps, you must off row and column clustering

Other parameters

In addition to the above function, you can also control other aesthetic parameters to control the fonts, border,

# heatmap with annotations
         border_color = FALSE,      # no border to cell
         fontsize_row = 5,          # row label font size
         fontsize_col = 5,          # column label font size 
         angle_col = 90,             # angle for column labels
         width = 5,                 # plot width in inches
         height = 7,                # plot height in inches
         na_col = "black",           # color of the cell with NA values
         legend = FALSE,            # to draw legend or not (TRUE/FALSE)
         filename = "heatmap.png"   # file name with path to save the heatmap 

with customized parameters

Tip: If you save an image using the filename parameter, use to close the plotting device

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