A Tidy Implementation of Heatmap • tidyHeatmap

Please have a look also to

nanny for tidy high-level data analysis and manipulation
tidygate for adding custom gate information to your tibble
tidybulk for tidy and modular transcriptomics analyses

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:

Modular annotation with just specifying column names
Custom grouping of rows is easy to specify providing a grouped tbl. For example df %>% group_by(...)
Labels size adjusted by row and column total number
Default use of Brewer and Viridis palettes

Functions/utilities available

Function	Description
`heatmap`	Plot base heatmap
`add_tile`	Add tile annotation to the heatmap
`add_point`	Add point annotation to the heatmap
`add_bar`	Add bar annotation to the heatmap
`add_line`	Add line annotation to the heatmap
`save_pdf`	Save 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 x 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

Plot

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 ) %>%
        add_tile(hp)

mtcars_heatmap

Save

mtcars_heatmap %>% save_pdf("mtcars_heatmap.pdf")

Grouping

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

mtcars_tidy %>%
    group_by(vs) %>%
    heatmap(`Car name`, Property, Value ) %>%
    add_tile(hp)

Custom palettes

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

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

Or a grid::colorRamp2 function for higher flexibility

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

Multiple groupings and annotations

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

Annotation types

This feature requires >= 0.99.20 version

“tile” (default), “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`
        ) %>%
    add_tile(condition) %>%
    add_point(activation) %>%
    add_tile(act) %>%
    add_bar(size) %>%
    add_line(age)

tidyHeatmap