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tidyHeatmap

Lifecycle:maturing DOI

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

Please have a look also to

website: stemangiola.github.io/tidyHeatmap

tidyHeatmap is a package that introduces tidy principles to the creation of information-rich heatmaps. This package uses ComplexHeatmap as graphical engine.

Advantages:

Functions/utilities available

Function Description
heatmap Plots base heatmap
add_tile Adds tile annotation to the heatmap
add_point Adds point annotation to the heatmap
add_bar Adds bar annotation to the heatmap
add_line Adds line annotation to 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
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

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
## # … with 278 more rows

Plotting

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

mtcars

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

mtcars_heatmap

Saving

mtcars_heatmap |> save_pdf("mtcars_heatmap.pdf")

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 |> 
    group_by(vs, property_group) |>
    heatmap(`Car name`, Property, Value,    scale = "row"   ) |>
    add_tile(hp)

We can provide colour palettes to groupings

mtcars_tidy_groupings |> 
    group_by(vs, property_group) |>
    heatmap(
        `Car name`, Property, Value ,   
        scale = "row",
        palette_grouping = list(
            
            # For first grouping (vs)
            c("#66C2A5", "#FC8D62"), 
            
            # For second grouping (property_group)
            c("#b58b4c", "#74a6aa")
        )
    ) |>
    add_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 grid::colorRamp2 function for tile annotation too

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

Multiple groupings and annotations

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

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

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

Annotation types

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

# Create some more data points
pasilla_plus <- 
    tidyHeatmap::pasilla |>
    dplyr::mutate(act = 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"
    ) |>
    add_tile(condition) |>
    add_point(activation) |>
    add_tile(act) |>
    add_bar(size) |>
    add_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"
    ) |>
    add_tile(condition, size = unit(0.3, "cm"), annotation_name_gp= gpar(fontsize = 8)) |>
    add_point(activation, size = unit(0.3, "cm"),   annotation_name_gp= gpar(fontsize = 8)) |>
    add_tile(act, size = unit(0.3, "cm"),   annotation_name_gp= gpar(fontsize = 8)) |>
    add_bar(size, size = unit(0.3, "cm"),   annotation_name_gp= gpar(fontsize = 8)) |>
    add_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)) |>
    
    heatmap(
        .column = sample,
        .row = symbol,
        .value = `count normalised adjusted`,   
        scale = "row"
    ) |> 
    layer_point(
        `count normalised adjusted log` > 6 & sample == "untreated3" 
    )

Adding heatmap side-by-side

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

p_heatmap + p_heatmap

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)
mtcars_tidy |> 
    mutate(`Car name` = 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` = 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` = 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)
    )

External ComplexHeatmap functionalities

ComplexHeatmap has some graphical functionalities that are not included in the standard functional framework

Chainging side of legends

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

Using patchwork to integrate heatmaps

library(ggplot2)
library(patchwork)

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

p_ggplot = tibble(value = 1:10) %>% ggplot(aes(value)) + geom_density()

wrap_heatmap(p_heatmap) + 
    p_ggplot +
    wrap_heatmap(p_heatmap) + 
    plot_layout(width = c(1, 0.3, 1))

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.