Overview of the tidyHeatmap package

Functions/utilities available

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).

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
    gather(Property, Value, -`Car name`, -hp, -vs)

mtcars_tidy

## # A tibble: 288 x 5
##    `Car name`           hp    vs Property  Value
##    <chr>             <dbl> <dbl> <chr>     <dbl>
##  1 Mazda RX4           110     0 mpg       0.151
##  2 Mazda RX4 Wag       110     0 mpg       0.151
##  3 Datsun 710           93     1 mpg       0.450
##  4 Hornet 4 Drive      110     1 mpg       0.217
##  5 Hornet Sportabout   175     0 mpg      -0.231
##  6 Valiant             105     1 mpg      -0.330
##  7 Duster 360          245     0 mpg      -0.961
##  8 Merc 240D            62     1 mpg       0.715
##  9 Merc 230             95     1 mpg       0.450
## 10 Merc 280            123     1 mpg      -0.148
## # … 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)