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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  6
head(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  9235

The 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

Version Author Date
121244c Dave Tang 2026-02-25

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
)

Version Author Date
121244c Dave Tang 2026-02-25

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
)

Version Author Date
121244c Dave Tang 2026-02-25

Turning off clustering

Heatmap(
  data_subset_norm,
  name                  = "z-score",
  col                   = col_fun,
  cluster_rows          = FALSE,
  cluster_columns       = FALSE,
  column_title          = "No clustering"
)

Version Author Date
121244c Dave Tang 2026-02-25

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
)

Version Author Date
121244c Dave Tang 2026-02-25

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
)

Version Author Date
121244c Dave Tang 2026-02-25

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"
)

Version Author Date
121244c Dave Tang 2026-02-25

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"
)

Version Author Date
121244c Dave Tang 2026-02-25

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)"
)

Version Author Date
121244c Dave Tang 2026-02-25

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")
)

Version Author Date
121244c Dave Tang 2026-02-25

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")

Version Author Date
121244c Dave Tang 2026-02-25

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

Exporting to PNG

The basic pattern

ComplexHeatmap draws via the grid graphics system, so you must open a graphics device, call draw(), and then close the device with dev.off(). Calling png() alone and then printing the heatmap object works too, but using draw() gives you control over legend placement at export time.

png(
  filename = "heatmap.png",
  width    = 8,      # inches
  height   = 6,      # inches
  units    = "in",
  res      = 300,    # dots per inch — 300 is standard for publication
  bg       = "white" # explicit white background (avoids transparency issues)
)
draw(ht_final, heatmap_legend_side = "bottom", annotation_legend_side = "bottom")
dev.off()

Key parameters

Parameter Typical values Notes
width / height 6–14 In the units given by units
units "in", "cm", "px" "in" is most intuitive for print
res 72, 150, 300, 600 See table below
bg "white", "transparent" Journals usually require white
pointsize 8–14 Base font size in points; default is 12

Common res (DPI) values and their intended use:

DPI Use case
72 Screen / web preview
150 Draft print, presentations
300 Standard publication quality
600 High-resolution print / microscopy figures

Sizing the image to the heatmap content

A heatmap with many rows needs more height than one with few rows. A simple rule of thumb: allocate ~0.25 inches per row and ~0.6 inches per column, then add a fixed margin for titles, legends, and annotations.

n_rows <- nrow(mat_z)
n_cols <- ncol(mat_z)

img_width  <- n_cols * 0.6 + 3   # 3 in of margin for row annotations + legend
img_height <- n_rows * 0.25 + 3  # 3 in of margin for col annotations + legend

png(
  filename = "heatmap_sized.png",
  width    = img_width,
  height   = img_height,
  units    = "in",
  res      = 300,
  bg       = "white"
)
draw(ht_final, heatmap_legend_side = "bottom", annotation_legend_side = "bottom")
dev.off()

For very large matrices (hundreds of rows) you may want a fixed height with show_row_names = FALSE rather than scaling linearly.

Adjusting font sizes for high-resolution output

At 300 DPI the default pointsize = 12 renders fine. If you increase res to 600 DPI the plot appears to shrink unless you increase pointsize to match, or enlarge width and height. For example, to maintain the same visual size at 600 DPI as a 300-DPI export:

png(
  filename  = "heatmap_600dpi.png",
  width     = img_width * 2,   # double the physical size ...
  height    = img_height * 2,
  units     = "in",
  res       = 600,             # ... at double the DPI = same pixel count
  pointsize = 16,              # larger base font to stay legible
  bg        = "white"
)
draw(ht_final, heatmap_legend_side = "bottom", annotation_legend_side = "bottom")
dev.off()

Vector formats (PDF and SVG)

For formats that require infinite scalability (e.g. figures submitted as PDF to a journal), replace png() with pdf() or svg(). These devices ignore res because they are resolution-independent.

pdf(
  file   = "heatmap.pdf",
  width  = img_width,
  height = img_height
)
draw(ht_final, heatmap_legend_side = "bottom", annotation_legend_side = "bottom")
dev.off()
svg(
  filename = "heatmap.svg",
  width    = img_width,
  height   = img_height
)
draw(ht_final, heatmap_legend_side = "bottom", annotation_legend_side = "bottom")
dev.off()

Exporting multiple heatmaps to one file

A concatenated heatmap (created with +) is itself a HeatmapList object and is drawn exactly the same way.

ht_list <- ht_raw + ht_norm   # the two-heatmap object built earlier

png(
  filename = "two_heatmaps.png",
  width    = 14,
  height   = 8,
  units    = "in",
  res      = 300,
  bg       = "white"
)
draw(ht_list)
dev.off()

Further reading

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