Functions/utilities available

Function	Description
`heatmap`	Plots base heatmap
`annotation_group`	Adds group annotation strips and grouping to the heatmap (replaces group_by)
`annotation_tile`	Adds tile annotation to the heatmap
`annotation_point`	Adds point annotation to the heatmap
`annotation_bar`	Adds bar annotation to the heatmap
`annotation_numeric`	Adds bar + number annotation to the heatmap
`annotation_line`	Adds line annotation to the heatmap
`layer_text`	Add layer of text on top of the heatmap
`layer_point`	Adds layer of symbols on top of the heatmap
`layer_square`	Adds layer of symbols on top of the heatmap
`layer_diamond`	Adds layer of symbols on top of the heatmap
`layer_arrow_up`	Adds layer of symbols on top of the heatmap
`layer_arrow_down`	Add layer of symbols on top of the heatmap
`layer_star`	Add layer of symbols on top of the heatmap
`layer_asterisk`	Add layer of symbols on top of the heatmap
`split_rows`	Splits the rows based on the dendogram
`split_columns`	Splits the columns based on the dendogram
`save_pdf`	Saves the PDF of the heatmap
`+`	Integrate heatmaps side-by-side
`as_ComplexHeatmap`	Convert the tidyHeatmap output to ComplexHeatmap for non-standard “drawing”
`wrap_heatmap`	Allows the integration with the `patchwork` package

Installation

To install the most up-to-date version

devtools::install_github("stemangiola/tidyHeatmap")

To install the most stable version (however please keep in mind that this package is under a maturing lifecycle stage)

install.packages("tidyHeatmap")

Contribution

If you want to contribute to the software, report issues or problems with the software or seek support please open an issue here

Input data frame

The heatmaps visualise a multi-element, multi-feature dataset, annotated with independent variables. Each observation is a element-feature pair (e.g., person-physical characteristics).

element	feature	value	independent_variables
`chr` or `fctr`	`chr` or `fctr`	`numeric`	…

Let’s transform the mtcars dataset into a tidy “element-feature-independent variables” data frame. Where the independent variables in this case are ‘hp’ and ‘vs’.

mtcars_tidy <- 
    mtcars |> 
    as_tibble(rownames="Car name") |> 
    
    # Scale
    mutate_at(vars(-`Car name`, -hp, -vs), scale) |>
    
    # tidyfy
    pivot_longer(cols = -c(`Car name`, hp, vs), names_to = "Property", values_to = "Value")

mtcars_tidy

## # A tibble: 288 × 5
##    `Car name`       hp    vs Property Value[,1]
##    <chr>         <dbl> <dbl> <chr>        <dbl>
##  1 Mazda RX4       110     0 mpg          0.151
##  2 Mazda RX4       110     0 cyl         -0.105
##  3 Mazda RX4       110     0 disp        -0.571
##  4 Mazda RX4       110     0 drat         0.568
##  5 Mazda RX4       110     0 wt          -0.610
##  6 Mazda RX4       110     0 qsec        -0.777
##  7 Mazda RX4       110     0 am           1.19 
##  8 Mazda RX4       110     0 gear         0.424
##  9 Mazda RX4       110     0 carb         0.735
## 10 Mazda RX4 Wag   110     0 mpg          0.151
## # ℹ 278 more rows

Plotting

For plotting, you simply pipe the input data frame into heatmap, specifying:

The rows, cols relative column names (mandatory)
The value column name (mandatory)
The annotations column name(s)

mtcars

mtcars_heatmap <- 
    mtcars_tidy |> 
    heatmap(`Car name`, Property, Value,    scale = "row"   ) |>
    annotation_tile(hp)

## tidyHeatmap says: If you use tidyHeatmap for scientific research, please cite: Mangiola, S. and Papenfuss, A.T., 2020. 'tidyHeatmap: an R package for modular heatmap production based on tidy principles.' Journal of Open Source Software. doi:10.21105/joss.02472.
## This message is displayed once per session.

mtcars_heatmap

Saving

mtcars_heatmap |> save_pdf("mtcars_heatmap.pdf")

Clustering

Choose alternative clustering distance and methods.

tidyHeatmap::pasilla |>
    
    heatmap(
        .column = sample,
        .row = symbol,
        .value = `count normalised adjusted`,   
        scale = "row",
        
        # Arguments passed to ComplexHeatmap 
        clustering_distance_rows = "manhattan",
        clustering_distance_columns = "manhattan",
        clustering_method_rows = "ward.D",
        clustering_method_columns = "ward.D"
    )

Grouping and splitting

We can easily group the data (one group per dimension maximum, at the moment only the vertical dimension is supported) with dplyr, and the heatmap will be grouped accordingly

# Make up more groupings
mtcars_tidy_groupings = 
    mtcars_tidy |>
    mutate(property_group = if_else(Property %in% c("cyl", "disp"), "Engine", "Other"))

mtcars_tidy_groupings |> 
    heatmap(`Car name`, Property, Value, scale = "row") |>
    annotation_group(vs, property_group) |>
    annotation_tile(hp)

You can provide colour palettes and other aesthetics to groupings

mtcars_tidy_groupings |> 
    heatmap(
        `Car name`, Property, Value ,  
        scale = "row"
    ) |>
    annotation_group(
        vs, property_group,
        palette_grouping = list(
            # For first grouping (vs)
            c("#66C2A5", "#FC8D62"), 
            # For second grouping (property_group)
            c("#b58b4c", "#74a6aa")
        ),
        group_label_fontsize = 14,
        show_group_name = TRUE,
        group_strip_height = grid::unit(20, "pt")
    ) |>
    annotation_tile(hp)

We can split based on the cladogram

mtcars_tidy |> 
    heatmap(`Car name`, Property, Value,    scale = "row"   ) |>
    split_rows(2) |>
    split_columns(2)

We can split on kmean clustering (using ComplexHeatmap options, it is stochastic)

mtcars_tidy |> 
    heatmap(
        `Car name`, Property, Value,    
        scale = "row",
        row_km = 2,
        column_km = 2
    )

Custom palettes

We can easily use custom palette, using strings, hexadecimal color character vector,

mtcars_tidy |> 
    heatmap(
        `Car name`, 
        Property, 
        Value,  
        scale = "row",
        palette_value = c("red", "white", "blue")
    )

A better-looking blue-to-red palette

mtcars_tidy |> 
    heatmap(
        `Car name`, 
        Property, 
        Value,  
        scale = "row",
        palette_value = circlize::colorRamp2(
            seq(-2, 2, length.out = 11), 
            RColorBrewer::brewer.pal(11, "RdBu")
        )
    )

Or a grid::colorRamp2 function for higher flexibility

mtcars_tidy |> 
    heatmap(
        `Car name`, 
        Property, 
        Value,  
        scale = "row",
        palette_value = circlize::colorRamp2(c(-2, -1, 0, 1, 2), viridis::magma(5))
    )

We can use custom colors for tile annotation

mtcars_tidy |> 
    heatmap(
        `Car name`, 
        Property, 
        Value,  
        scale = "row"
    ) |>
    annotation_tile(
        hp, 
        palette = c("red", "white", "blue")
    )

We can use grid::colorRamp2 function for tile annotation for specific color scales

mtcars_tidy |> 
    heatmap(
        `Car name`, 
        Property, 
        Value,  
        scale = "row"
    ) |>
    annotation_tile(
        hp, 
        palette = circlize::colorRamp2(c(0, 100, 200, 300), viridis::magma(4))
    )

Multiple groupings and annotations

tidyHeatmap::pasilla |>
    heatmap(
        .column = sample,
        .row = symbol,
        .value = `count normalised adjusted`,  
        scale = "row"
    ) |>
    annotation_group(location, type) |>
    annotation_tile(condition) |>
    annotation_tile(activation)

Example with custom aesthetics for annotation_group

tidyHeatmap::pasilla |>
    heatmap(
        .column = sample,
        .row = symbol,
        .value = `count normalised adjusted`,  
        scale = "row"
    ) |>
    annotation_group(
        location, type,
        group_label_fontsize = 12,
        group_strip_height = grid::unit(15, "pt"),
        show_group_name = FALSE
    ) |>
    annotation_tile(condition) |>
    annotation_tile(activation)

Remove legends, adding aesthetics to annotations in a modular fashion, using ComplexHeatmap arguments

tidyHeatmap::pasilla |>
    heatmap(
        .column = sample,
        .row = symbol,
        .value = `count normalised adjusted`,   
        scale = "row",
        show_heatmap_legend = FALSE
    ) |>
    annotation_tile(condition, show_legend = FALSE) |>
    annotation_tile(activation, show_legend = FALSE)

Annotation types

“tile”, “point”, “bar”, “line” and “numeric” are available

# Create some more data points
pasilla_plus <- 
    tidyHeatmap::pasilla |>
    dplyr::mutate(activation_2 = activation, activation_3 = activation) |> 
    tidyr::nest(data = -sample) |>
    dplyr::mutate(size = rnorm(n(), 4,0.5)) |>
    dplyr::mutate(age = runif(n(), 50, 200)) |>
    tidyr::unnest(data) 

# Plot
pasilla_plus |>
    heatmap(
        .column = sample,
        .row = symbol,
        .value = `count normalised adjusted`,   
        scale = "row"
    ) |>
    annotation_tile(condition) |>
    annotation_point(activation) |>
    annotation_numeric(activation_3) |>
    annotation_tile(activation_2) |>
    annotation_bar(size) |>
    annotation_line(age)

Annotation size

We can customise annotation sizes using the grid::unit(), and the size of their names using in-built ComplexHeatmap arguments

pasilla_plus |>
    heatmap(
        .column = sample,
        .row = symbol,
        .value = `count normalised adjusted`,   
        scale = "row"
    ) |>
    annotation_tile(condition, size = unit(0.3, "cm"),  annotation_name_gp= gpar(fontsize = 8)) |>
    annotation_point(activation, size = unit(0.3, "cm"),    annotation_name_gp= gpar(fontsize = 8)) |>
    annotation_tile(activation_2, size = unit(0.3, "cm"),   annotation_name_gp= gpar(fontsize = 8)) |>
    annotation_bar(size, size = unit(0.3, "cm"),    annotation_name_gp= gpar(fontsize = 8)) |>
    annotation_line(age, size = unit(0.3, "cm"),    annotation_name_gp= gpar(fontsize = 8))

Layer symbol

Add a layer on top of the heatmap

tidyHeatmap::pasilla |>
    
    # filter
    filter(symbol %in% head(unique(tidyHeatmap::pasilla$symbol), n = 10)) |>
    
    # Add dynamic size
    mutate(my_size = runif(n(), 1,5)) |> 
    
    heatmap(
        .column = sample,
        .row = symbol,
        .value = `count normalised adjusted`,   
        scale = "row"
    ) |> 
    layer_point(
        `count normalised adjusted log` > 6 & sample == "untreated3"
    ) |>
    layer_square(
        `count normalised adjusted log` > 6 & sample == "untreated2",
        .size = my_size
    ) |>
    layer_arrow_up(
        `count normalised adjusted log` > 6 & sample == "untreated1",
        .size = 4
    )

Layer text

Add a text layer on top of the heatmap

tidyHeatmap::pasilla |>
    
    # filter
    filter(symbol %in% head(unique(tidyHeatmap::pasilla$symbol), n = 10)) |>
    
    # Add dynamic text
    mutate(my_text = "mt", my_size = 7) |> 
    
    # Plot
    heatmap(
        .column = sample,
        .row = symbol,
        .value = `count normalised adjusted`,   
        scale = "row"
    ) |> 
    layer_text(
        `count normalised adjusted log` > 6 & sample == "untreated3", 
        .value = "a", 
        .size = 15
    ) |> 
    layer_text(
    `count normalised adjusted log` > 6 & sample == "untreated2", 
    .value = my_text,
    .size = my_size
)

Adding heatmap side-by-side

p_heatmap = heatmap(mtcars_tidy, `Car name`, Property, Value, scale = "row") 

p_heatmap + p_heatmap

Add more than two heatmaps

Note:

Due to limitations in R’s method dispatch, adding more than two tidyHeatmap heatmaps using the + operator (e.g. p_heatmap + p_heatmap + p_heatmap) may result in an error or unexpected behavior.

To robustly combine more than two heatmaps, convert each to a ComplexHeatmap object first, this will use the + functionality of ComplexHeatmap. This ensures that all objects are compatible and can be combined using ComplexHeatmap’s native addition functionality.

as_ComplexHeatmap(p_heatmap) +
  as_ComplexHeatmap(p_heatmap) +
  as_ComplexHeatmap(p_heatmap)

Using patchwork to integrate across heatmaps and including ggplots

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.5.1

library(patchwork)

## Warning: package 'patchwork' was built under R version 4.5.1

p_heatmap =
    mtcars_tidy |> 
    heatmap(
        `Car name`, Property, Value,    
        scale = "row", 
            show_heatmap_legend = FALSE,
        row_names_gp = gpar(fontsize = 7)
    ) 

p_ggplot = data.frame(value = 1:10) |> ggplot(aes(value)) + geom_density()

wrap_heatmap(p_heatmap) + 
    p_ggplot +
    
    # Add padding for better aesthetics
    wrap_heatmap(
        p_heatmap,
        padding = grid::unit(c(-30, -0, -0, -10), "points" ),
        clip = FALSE
    ) + 
    plot_layout(width = c(1, 0.3, 1))

ComplexHeatmap further styling

Add cell borders

mtcars_tidy |> 
    heatmap(
        `Car name`, Property, Value,    
        scale = "row", 
        rect_gp = grid::gpar(col = "#161616", lwd = 0.5)
    )

Drop row clustering

mtcars_tidy |> 
    heatmap(
        `Car name`, Property, Value,    
        scale = "row", 
        cluster_rows = FALSE
    )

Reorder rows elements

library(forcats)

## Warning: package 'forcats' was built under R version 4.5.1

mtcars_tidy |> 
    mutate(`Car name` = forcats::fct_reorder(`Car name`, `Car name`, .desc = TRUE)) %>% 
    heatmap(
        `Car name`, Property, Value,    
        scale = "row", 
        cluster_rows = FALSE
    )

Size of dendrograms

mtcars_tidy |> 
    mutate(`Car name` = forcats::fct_reorder(`Car name`, `Car name`, .desc = TRUE)) %>% 
    heatmap(
        `Car name`, Property, Value,    
        scale = "row", 
        column_dend_height = unit(0.2, "cm"), 
        row_dend_width = unit(0.2, "cm")
    )

Size of rows/columns titles and names

mtcars_tidy |> 
    mutate(`Car name` = forcats::fct_reorder(`Car name`, `Car name`, .desc = TRUE)) %>% 
    heatmap(
        `Car name`, Property, Value,    
        scale = "row", 
        row_names_gp = gpar(fontsize = 7),
        column_names_gp = gpar(fontsize = 7),
        column_title_gp = gpar(fontsize = 7),
        row_title_gp = gpar(fontsize = 7)
    )

Remove row and column titles

You can completely remove the row and column titles by setting them to empty strings:

mtcars_tidy |> 
    mutate(`Car name` = forcats::fct_reorder(`Car name`, `Car name`, .desc = TRUE)) |> 
    heatmap(
        `Car name`, Property, Value,    
        scale = "row", 
        row_title = "",      # Remove row title
        column_title = ""    # Remove column title
    )

Align numeric annotation

This can be done only for annotation_numeric because of ComplexHeatmap requirements (?ComplexHeatmap::anno_numeric)

mtcars_tidy |> 
    mutate(`Car name` = forcats::fct_reorder(`Car name`, `Car name`, .desc = TRUE)) %>% 
    heatmap(
        `Car name`, Property, Value,    
        scale = "row"
    ) |> 
  annotation_numeric(hp, align_to="right")

External `ComplexHeatmap` functionalities

ComplexHeatmap has some graphical functionalities that are not included in the standard functional framework. We can use as_ComplexHeatmap to convert our output before applying drawing options.

Chainging side of legends

heatmap(mtcars_tidy, `Car name`, Property, Value, scale = "row" ) %>%
    as_ComplexHeatmap() %>%
    ComplexHeatmap::draw(heatmap_legend_side = "left"   )

Add title using `draw` from `ComplexHeatmap`

mtcars_tidy |> 
    heatmap(`Car name`, Property, Value,    scale = "row"   ) |>
    as_ComplexHeatmap() |>
    ComplexHeatmap::draw(
        column_title = "TITLE", 
        column_title_gp = gpar(fontsize = 16)
    )

Add title using `ggtitle` from `ggplot2`

mtcars_tidy |> 
    heatmap(`Car name`, Property, Value,  scale = "row" ) |>
    wrap_heatmap() +
        ggplot2::ggtitle("TITLE")

Session Info

sessionInfo()

## R version 4.5.0 (2025-04-11)
## Platform: x86_64-apple-darwin20
## Running under: macOS Sonoma 14.6.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
## 
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: Australia/Adelaide
## tzcode source: internal
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] forcats_1.0.1      patchwork_1.3.2    ggplot2_4.0.0      tidyHeatmap_1.12.3
## [5] tidyr_1.3.1        dplyr_1.1.4       
## 
## loaded via a namespace (and not attached):
##  [1] viridis_0.6.5         utf8_1.2.6            sass_0.4.10          
##  [4] generics_0.1.4        shape_1.4.6.1         digest_0.6.37        
##  [7] magrittr_2.0.4        evaluate_1.0.5        RColorBrewer_1.1-3   
## [10] iterators_1.0.14      circlize_0.4.16       fastmap_1.2.0        
## [13] foreach_1.5.2         doParallel_1.0.17     jsonlite_2.0.0       
## [16] GlobalOptions_0.1.2   gridExtra_2.3         ComplexHeatmap_2.25.2
## [19] purrr_1.1.0           viridisLite_0.4.2     scales_1.4.0         
## [22] codetools_0.2-20      jquerylib_0.1.4       cli_3.6.5            
## [25] rlang_1.1.6           crayon_1.5.3          withr_3.0.2          
## [28] cachem_1.1.0          yaml_2.3.10           tools_4.5.0          
## [31] parallel_4.5.0        colorspace_2.1-2      GetoptLong_1.0.5     
## [34] BiocGenerics_0.55.4   vctrs_0.6.5           R6_2.6.1             
## [37] png_0.1-8             magick_2.9.0          matrixStats_1.5.0    
## [40] stats4_4.5.0          lifecycle_1.0.4       S4Vectors_0.47.4     
## [43] IRanges_2.43.5        clue_0.3-66           cluster_2.1.8.1      
## [46] dendextend_1.19.1     pkgconfig_2.0.3       gtable_0.3.6         
## [49] pillar_1.11.1         bslib_0.9.0           Rcpp_1.1.0           
## [52] glue_1.8.0            xfun_0.53             tibble_3.3.0         
## [55] tidyselect_1.2.1      rstudioapi_0.17.1     knitr_1.50           
## [58] farver_2.1.2          rjson_0.2.23          htmltools_0.5.8.1    
## [61] labeling_0.4.3        rmarkdown_2.30        Cairo_1.6-5          
## [64] compiler_4.5.0        S7_0.2.0

Overview of the tidyHeatmap package

Stefano Mangiola

2025-10-25

Citation

Functions/utilities available

Installation

Contribution

Input data frame

Plotting

Saving

Clustering

Grouping and splitting

Custom palettes

Multiple groupings and annotations

Annotation types

Annotation size

Layer symbol

Layer text

Adding heatmap side-by-side

Using patchwork to integrate across heatmaps and including ggplots

ComplexHeatmap further styling

Add cell borders

Drop row clustering

Reorder rows elements

Size of dendrograms

Size of rows/columns titles and names

Remove row and column titles

Align numeric annotation

External `ComplexHeatmap` functionalities

Chainging side of legends

Add title using `draw` from `ComplexHeatmap`

Add title using `ggtitle` from `ggplot2`

Session Info

Overview of the tidyHeatmap package

Stefano Mangiola

2025-10-25

Citation

Functions/utilities available

Installation

Contribution

Input data frame

Plotting

Saving

Clustering

Grouping and splitting

Custom palettes

Multiple groupings and annotations

Annotation types

Annotation size

Layer symbol

Layer text

Adding heatmap side-by-side

Using patchwork to integrate across heatmaps and including ggplots

ComplexHeatmap further styling

Add cell borders

Drop row clustering

Reorder rows elements

Size of dendrograms

Size of rows/columns titles and names

Remove row and column titles

Align numeric annotation

External ComplexHeatmap functionalities

Chainging side of legends

Add title using draw from ComplexHeatmap

Add title using ggtitle from ggplot2

Session Info

External `ComplexHeatmap` functionalities

Add title using `draw` from `ComplexHeatmap`

Add title using `ggtitle` from `ggplot2`