Making a heatmap in R with the ComplexHeatmap package
2026-02-25
Last updated: 2026-02-25
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| Rmd | 8915164 | Dave Tang | 2025-10-24 | You can make UpSet plots with ComplexHeatmap |
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| Rmd | 3e13eb5 | Dave Tang | 2023-11-01 | ComplexHeatmap |
Background
ComplexHeatmap
is an R/Bioconductor package developed by Zuguang Gu that provides a
highly flexible framework for drawing heatmaps. While simpler packages
like pheatmap or heatmap2 are fine for quick
visualisations, ComplexHeatmap excels when you need:
- Multiple heatmaps placed side-by-side with a shared colour legend.
- Rich annotations along rows and/or columns — categorical, continuous, bar plots, box plots, density plots, text, and more.
- Heatmap splitting (slicing) by k-means clusters or a grouping factor.
- UpSet plots as an alternative to Venn diagrams.
- Fine-grained control over colours, dendrograms, legends, and layout.
The comprehensive reference book is freely available at https://jokergoo.github.io/ComplexHeatmap-reference/book/.
Installation
Install the stable version from Bioconductor.
BiocManager::install("ComplexHeatmap")
# circlize is used for colorRamp2(), a flexible colour mapping helper
install.packages("circlize")Data
Download example RNA-seq count data (TagSeq format). We keep only genes with a high total count so the heatmap is not too crowded.
example_file <- "https://davetang.org/file/TagSeqExample.tab"
data <- read.delim(example_file, header = TRUE, row.names = "gene")
data_subset <- as.matrix(data[rowSums(data) > 50000, ])
dim(data_subset)[1] 49 6head(data_subset) T1a T1b T2 T3 N1 N2
Gene_00562 32314 29693 66140 17973 47994 30878
Gene_02115 15261 23301 1944 4578 4087 1072
Gene_02296 6730 5389 14491 15620 29445 14653
Gene_02420 5827 3938 18800 5592 26430 9071
Gene_02800 5961 7833 10303 15709 17405 10946
Gene_03194 7611 6806 13506 5727 25020 9235The six columns are biological samples; each row is a gene. Values are raw counts.
Z-score scaling
Why scale?
Raw counts vary enormously between genes — a highly-expressed gene can have counts hundreds of times larger than a lowly-expressed one. If we colour the heatmap on the raw scale, the colour gradient is dominated by the most abundant genes and most rows look uniformly blue (low) regardless of their actual expression pattern.
Z-score row scaling transforms each row so that it has mean 0 and standard deviation 1:
\[z_i = \frac{x_i - \bar{x}}{\text{sd}(x)}\]
After this transformation the colour of each cell reflects how far that sample is from the gene’s own mean, not the absolute expression level. This makes cross-gene patterns — e.g. a cluster of genes all up-regulated in one condition — visually apparent.
Row scaling vs column scaling
| Direction | What is normalised | Typical use |
|---|---|---|
Row (apply(mat, 1, ...)) |
Each gene across samples | Compare sample patterns per gene |
Column (apply(mat, 2, ...)) |
Each sample across genes | Compare gene patterns per sample (rare) |
Row scaling is by far the more common choice in genomics.
cal_z_score <- function(x) (x - mean(x)) / sd(x)
# Apply row-wise: MARGIN = 1
data_subset_norm <- t(apply(data_subset, 1, cal_z_score))
# Sanity check — each row should now have mean ≈ 0 and sd ≈ 1
round(head(rowMeans(data_subset_norm)), 10)Gene_00562 Gene_02115 Gene_02296 Gene_02420 Gene_02800 Gene_03194
0 0 0 0 0 0 round(head(apply(data_subset_norm, 1, sd)), 10)Gene_00562 Gene_02115 Gene_02296 Gene_02420 Gene_02800 Gene_03194
1 1 1 1 1 1 Raw vs scaled side-by-side
Placing the raw and z-scored heatmaps next to each other makes the benefit of scaling clear.
ht_raw <- Heatmap(data_subset, column_title = "Raw counts", name = "counts")
ht_norm <- Heatmap(data_subset_norm, column_title = "Z-score", name = "z-score")
ht_raw + ht_norm
Custom colour scale for Z-scores
The default blue-to-red diverging palette works well for z-scores,
but you can be explicit with colorRamp2() from the
circlize package. Symmetric limits (e.g. −2 to +2)
ensure that zero (the gene mean) always maps to white.
col_fun <- colorRamp2(c(-2, 0, 2), c("blue", "white", "red"))
Heatmap(
data_subset_norm,
name = "z-score",
col = col_fun,
column_title = "Z-score normalised expression",
row_title = paste(nrow(data_subset_norm), "genes"),
column_names_rot = 45
)
Values beyond ±2 are clipped to the extreme colours, which is usually desirable — it prevents a single outlier gene from washing out the rest of the palette.
Basic heatmap options
Titles and legend name
column_title / row_title label the heatmap
body; name is the title shown above the colour legend.
Heatmap(
data_subset_norm,
name = "z-score",
column_title = "Samples",
row_title = "Genes",
col = col_fun
)
Turning off clustering
Heatmap(
data_subset_norm,
name = "z-score",
col = col_fun,
cluster_rows = FALSE,
cluster_columns = FALSE,
column_title = "No clustering"
)
Annotations
Annotations are one of the most powerful features of ComplexHeatmap. They sit alongside the heatmap body and convey additional metadata about samples (column annotations) or genes (row annotations).
We will build a small synthetic dataset with clear group structure to make the annotations easy to read.
set.seed(42)
n_genes <- 20
n_samples <- 12
# Simulate two conditions with 6 replicates each
condition <- rep(c("Control", "Treatment"), each = 6)
batch <- rep(c("Batch1", "Batch2", "Batch1", "Batch2", "Batch1", "Batch2"), 2)
# Generate a matrix where Treatment genes have higher expression
mat <- matrix(rnorm(n_genes * n_samples), nrow = n_genes, ncol = n_samples)
mat[1:10, 7:12] <- mat[1:10, 7:12] + 2 # first 10 genes up in Treatment
rownames(mat) <- paste0("Gene", seq_len(n_genes))
colnames(mat) <- paste0(condition, "_", seq_len(n_samples))Column (sample) annotations
HeatmapAnnotation() accepts:
- Named character/factor vectors → drawn as coloured tiles (categorical).
- Named numeric vectors → drawn as a gradient (continuous).
anno_*()functions → barplots, boxplots, density, text, etc.
# Custom colour maps for categorical annotations
cond_colours <- c(Control = "#4393C3", Treatment = "#D6604D")
batch_colours <- c(Batch1 = "#F1A340", Batch2 = "#998EC3")
col_ha <- HeatmapAnnotation(
Condition = condition,
Batch = batch,
MeanExpr = colMeans(mat), # continuous: shown as gradient
col = list(
Condition = cond_colours,
Batch = batch_colours,
MeanExpr = colorRamp2(c(-1, 0, 1), c("white", "grey60", "black"))
),
annotation_name_side = "left"
)
Heatmap(
mat,
name = "expr",
top_annotation = col_ha,
column_title = "Samples with column annotations",
show_column_names = FALSE
)
Row (gene) annotations
rowAnnotation() works the same way but applies to rows.
A common use case is to annotate genes with their log-fold change or
membership in a pathway.
gene_group <- rep(c("Up", "Down"), each = 10)
lfc <- rnorm(n_genes, mean = ifelse(gene_group == "Up", 1.5, -1.5), sd = 0.5)
row_ha <- rowAnnotation(
Group = gene_group,
LFC = lfc,
col = list(
Group = c(Up = "#D6604D", Down = "#4393C3"),
LFC = colorRamp2(c(-3, 0, 3), c("blue", "white", "red"))
),
annotation_name_side = "top"
)
Heatmap(
mat,
name = "expr",
top_annotation = col_ha,
right_annotation = row_ha,
column_title = "Heatmap with column and row annotations",
show_column_names = FALSE
)
Bar plot and box plot annotations
anno_barplot() and anno_boxplot() let you
display summary statistics directly alongside the heatmap, saving the
need for a separate figure.
col_ha2 <- HeatmapAnnotation(
Condition = condition,
MeanExpr = anno_barplot(colMeans(mat), height = unit(2, "cm")),
col = list(Condition = cond_colours)
)
row_ha2 <- rowAnnotation(
BoxExpr = anno_boxplot(mat, width = unit(3, "cm"))
)
Heatmap(
mat,
name = "expr",
top_annotation = col_ha2,
right_annotation = row_ha2,
show_column_names = FALSE,
column_title = "Bar and box plot annotations"
)
Text annotations
anno_text() is useful for labelling a small number of
important genes or samples directly on the plot rather than relying on
axis labels.
# Highlight the top 5 most variable genes
gene_vars <- apply(mat, 1, var)
top5 <- names(sort(gene_vars, decreasing = TRUE))[1:5]
gene_label <- ifelse(rownames(mat) %in% top5, rownames(mat), "")
row_ha3 <- rowAnnotation(
Label = anno_text(gene_label, gp = gpar(fontsize = 9, fontface = "bold"))
)
Heatmap(
mat,
name = "expr",
right_annotation = row_ha3,
show_row_names = FALSE,
show_column_names = FALSE,
column_title = "Text annotation for top variable genes"
)
Heatmap splitting
Splitting breaks the heatmap into visual blocks, making clusters easier to interpret. You can split by k-means clustering or by an explicit grouping factor.
K-means splitting
Heatmap(
mat,
name = "expr",
top_annotation = col_ha,
row_km = 2, # 2 row clusters
column_km = 2, # 2 column clusters
show_column_names = FALSE,
column_title = "K-means split (k=2)"
)
Splitting by a factor
When you already know the grouping (e.g. condition), pass it directly. This guarantees that samples from the same group are adjacent.
Heatmap(
mat,
name = "expr",
top_annotation = col_ha,
column_split = condition, # columns split by condition
row_split = gene_group, # rows split by gene group
show_column_names = FALSE,
column_title_gp = gpar(fontsize = 10, fontface = "bold"),
row_title_gp = gpar(fontsize = 10, fontface = "bold")
)
Putting it all together
A publication-ready heatmap combining z-score scaling, colour scale, full annotations, and factor-based splitting.
mat_z <- t(apply(mat, 1, cal_z_score))
col_ha_final <- HeatmapAnnotation(
Condition = condition,
Batch = batch,
col = list(
Condition = cond_colours,
Batch = batch_colours
),
annotation_name_side = "left",
show_legend = TRUE
)
row_ha_final <- rowAnnotation(
Group = gene_group,
LFC = lfc,
col = list(
Group = c(Up = "#D6604D", Down = "#4393C3"),
LFC = colorRamp2(c(-3, 0, 3), c("blue", "white", "red"))
),
annotation_name_side = "top"
)
ht_final <- Heatmap(
mat_z,
name = "z-score",
col = colorRamp2(c(-2, 0, 2), c("blue", "white", "red")),
top_annotation = col_ha_final,
right_annotation = row_ha_final,
column_split = condition,
row_split = gene_group,
show_column_names = FALSE,
column_title_gp = gpar(fontsize = 11, fontface = "bold"),
row_title_gp = gpar(fontsize = 11, fontface = "bold"),
heatmap_legend_param = list(
title = "z-score",
at = c(-2, -1, 0, 1, 2),
legend_direction = "horizontal"
)
)
draw(ht_final, heatmap_legend_side = "bottom", annotation_legend_side = "bottom")
UpSet plot
ComplexHeatmap has an implementation of UpSet plots that scales better than Venn diagrams for more than three sets.
Three lists.
set.seed(123)
lt <- list(a = sample(letters, 10),
b = sample(letters, 15),
c = sample(letters, 20))
m <- make_comb_mat(lt)
UpSet(m)
| Version | Author | Date |
|---|---|---|
| d10d811 | Dave Tang | 2025-10-24 |
union mode: 1 means in that set and 0 is not taken into account. When there are multiple 1, the relationship is OR. Then, 1 1 0 means a set of elements in set A or B, and they can also in C or not in C (union(A, B)). Under this mode, the seven combination sets can overlap.
m = make_comb_mat(lt, mode = "union")
UpSet(m)
| Version | Author | Date |
|---|---|---|
| d10d811 | Dave Tang | 2025-10-24 |
With colours.
UpSet(m, comb_col = c(rep(2, 3), rep(3, 3), 1))
| Version | Author | Date |
|---|---|---|
| d10d811 | Dave Tang | 2025-10-24 |
Compare two UpSet plots.
set.seed(123)
lt1 = list(a = sample(letters, 10),
b = sample(letters, 15),
c = sample(letters, 20))
m1 = make_comb_mat(lt1)
set.seed(456)
lt2 = list(a = sample(letters, 10),
b = sample(letters, 15),
c = sample(letters, 20))
m2 = make_comb_mat(lt2)
max1 = max(c(set_size(m1), set_size(m2)))
max2 = max(c(comb_size(m1), comb_size(m2)))
UpSet(m1, top_annotation = upset_top_annotation(m1, ylim = c(0, max2)),
right_annotation = upset_right_annotation(m1, ylim = c(0, max1)),
column_title = "UpSet1") +
UpSet(m2, top_annotation = upset_top_annotation(m2, ylim = c(0, max2)),
right_annotation = upset_right_annotation(m2, ylim = c(0, max1)),
column_title = "UpSet2")
| Version | Author | Date |
|---|---|---|
| d10d811 | Dave Tang | 2025-10-24 |
Further reading
- ComplexHeatmap Complete Reference — the authoritative guide by the package author.
- Bioconductor package page — vignettes and citation.
colorRamp2()from the circlize package is the recommended way to define colour scales.
Session info
sessionInfo()R version 4.5.2 (2025-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] circlize_0.4.17 ComplexHeatmap_2.26.1 workflowr_1.7.2
loaded via a namespace (and not attached):
[1] generics_0.1.4 sass_0.4.10 shape_1.4.6.1
[4] stringi_1.8.7 digest_0.6.39 magrittr_2.0.4
[7] RColorBrewer_1.1-3 evaluate_1.0.5 iterators_1.0.14
[10] fastmap_1.2.0 foreach_1.5.2 doParallel_1.0.17
[13] rprojroot_2.1.1 jsonlite_2.0.0 processx_3.8.6
[16] whisker_0.4.1 GlobalOptions_0.1.3 ps_1.9.1
[19] promises_1.5.0 httr_1.4.8 codetools_0.2-20
[22] jquerylib_0.1.4 cli_3.6.5 rlang_1.1.7
[25] crayon_1.5.3 cachem_1.1.0 yaml_2.3.12
[28] otel_0.2.0 tools_4.5.2 parallel_4.5.2
[31] colorspace_2.1-2 httpuv_1.6.16 GetoptLong_1.1.0
[34] BiocGenerics_0.56.0 vctrs_0.7.1 R6_2.6.1
[37] png_0.1-8 stats4_4.5.2 matrixStats_1.5.0
[40] lifecycle_1.0.5 git2r_0.36.2 stringr_1.6.0
[43] S4Vectors_0.48.0 fs_1.6.6 IRanges_2.44.0
[46] clue_0.3-67 cluster_2.1.8.1 pkgconfig_2.0.3
[49] callr_3.7.6 pillar_1.11.1 bslib_0.10.0
[52] later_1.4.7 glue_1.8.0 Rcpp_1.1.1
[55] xfun_0.56 tibble_3.3.1 rstudioapi_0.18.0
[58] knitr_1.51 rjson_0.2.23 htmltools_0.5.9
[61] rmarkdown_2.30 compiler_4.5.2 getPass_0.2-4
sessionInfo()R version 4.5.2 (2025-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] circlize_0.4.17 ComplexHeatmap_2.26.1 workflowr_1.7.2
loaded via a namespace (and not attached):
[1] generics_0.1.4 sass_0.4.10 shape_1.4.6.1
[4] stringi_1.8.7 digest_0.6.39 magrittr_2.0.4
[7] RColorBrewer_1.1-3 evaluate_1.0.5 iterators_1.0.14
[10] fastmap_1.2.0 foreach_1.5.2 doParallel_1.0.17
[13] rprojroot_2.1.1 jsonlite_2.0.0 processx_3.8.6
[16] whisker_0.4.1 GlobalOptions_0.1.3 ps_1.9.1
[19] promises_1.5.0 httr_1.4.8 codetools_0.2-20
[22] jquerylib_0.1.4 cli_3.6.5 rlang_1.1.7
[25] crayon_1.5.3 cachem_1.1.0 yaml_2.3.12
[28] otel_0.2.0 tools_4.5.2 parallel_4.5.2
[31] colorspace_2.1-2 httpuv_1.6.16 GetoptLong_1.1.0
[34] BiocGenerics_0.56.0 vctrs_0.7.1 R6_2.6.1
[37] png_0.1-8 stats4_4.5.2 matrixStats_1.5.0
[40] lifecycle_1.0.5 git2r_0.36.2 stringr_1.6.0
[43] S4Vectors_0.48.0 fs_1.6.6 IRanges_2.44.0
[46] clue_0.3-67 cluster_2.1.8.1 pkgconfig_2.0.3
[49] callr_3.7.6 pillar_1.11.1 bslib_0.10.0
[52] later_1.4.7 glue_1.8.0 Rcpp_1.1.1
[55] xfun_0.56 tibble_3.3.1 rstudioapi_0.18.0
[58] knitr_1.51 rjson_0.2.23 htmltools_0.5.9
[61] rmarkdown_2.30 compiler_4.5.2 getPass_0.2-4