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Overview of the tidyHeatmap package • tidyHeatmap

Citation

Mangiola et al., (2020). tidyHeatmap: an R package for modular heatmap production based on tidy principles. Journal of Open Source Software, 5(52), 2472, https://doi.org/10.21105/joss.02472

tidyHeatmap is a package that introduces tidy principles to the creation of information-rich heatmaps. This package uses ComplexHeatmap as graphical engine. website: stemangiola.github.io/tidyHeatmap

Advantages:

Modular annotation with just specifying column names
Custom grouping of rows/columns is easy to specify with annotation_group, e.g. heatmap(...) |> annotation_group(...)
Labels size adjusted by row and column total number
Default use of Brewer and Viridis palettes

Functions/utilities available 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

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

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).

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

## Warning: Using one column matrices in `filter()` was deprecated in dplyr 1.1.0.
## ℹ Please use one dimensional logical vectors instead.
## ℹ The deprecated feature was likely used in the tidyHeatmap package.
##   Please report the issue at
##   <https://github.com/stemangiola/tidyHeatmap/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
##   always returns an ungrouped data frame and adjust accordingly.
## ℹ The deprecated feature was likely used in the dplyr package.
##   Please report the issue at <https://github.com/tidyverse/dplyr/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

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)

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

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)

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

We can split based on the cladogram

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

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
    )

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

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")
    )

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

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")
        )
    )

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

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))
    )

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

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")
    )

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

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

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

Multiple groupings and annotations

Example with custom aesthetics for annotation_group

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

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.

Using patchwork to integrate across heatmaps and including ggplots

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

ComplexHeatmap further styling Add cell borders

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

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

Drop row clustering

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

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

Reorder rows elements

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

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")
    )

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

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)
    )

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

Align numeric annotation

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

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

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.

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

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)
    )

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

Add title using ggtitle from ggplot2

## Warning in as_mapper(.f1)(.x): to_matrix says: there are NON-numerical columns,
## the matrix will NOT be numerical

Session Info

## R version 4.5.1 (2025-06-13)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 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: UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] forcats_1.0.0      patchwork_1.3.1    ggplot2_3.5.2      tidyHeatmap_1.12.1
## [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_2.0.3        evaluate_1.0.4        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      textshaping_1.0.1     jquerylib_0.1.4      
## [25] cli_3.6.5             rlang_1.1.6           crayon_1.5.3         
## [28] withr_3.0.2           cachem_1.1.0          yaml_2.3.10          
## [31] tools_4.5.1           parallel_4.5.1        colorspace_2.1-1     
## [34] GetoptLong_1.0.5      BiocGenerics_0.55.0   vctrs_0.6.5          
## [37] R6_2.6.1              png_0.1-8             matrixStats_1.5.0    
## [40] stats4_4.5.1          lifecycle_1.0.4       S4Vectors_0.47.0     
## [43] fs_1.6.6              htmlwidgets_1.6.4     IRanges_2.43.0       
## [46] clue_0.3-66           cluster_2.1.8.1       ragg_1.4.0           
## [49] dendextend_1.19.1     pkgconfig_2.0.3       desc_1.4.3           
## [52] gtable_0.3.6          pkgdown_2.1.3         pillar_1.11.0        
## [55] bslib_0.9.0           glue_1.8.0            systemfonts_1.2.3    
## [58] xfun_0.52             tibble_3.3.0          tidyselect_1.2.1     
## [61] knitr_1.50            farver_2.1.2          rjson_0.2.23         
## [64] htmltools_0.5.8.1     labeling_0.4.3        rmarkdown_2.29       
## [67] compiler_4.5.1

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