This package is a tidy wrapper of the package ComplexHeatmap. The goal of this package is to interface a tidy data frame with this powerful tool.

Some of the advantages are:

• Row and/or columns colour annotations are easy to integrate just specifying one parameter (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

Input data frame

The input data frameha sto be a tidy tbl of this form

pasilla

## # A tibble: 504 x 6
##    sample     symbol `count normalised adjust… condition type       location    
##    <chr>      <fct>                      <int> <fct>     <fct>      <chr>       
##  1 treated1   Kal1                          37 treated   single-re… Secretory   
##  2 treated2   Kal1                          41 treated   paired-end Secretory   
##  3 treated3   Kal1                          50 treated   paired-end Secretory   
##  4 untreated1 Kal1                        1127 untreated single-re… Secretory   
##  5 untreated2 Kal1                        1046 untreated single-re… Secretory   
##  6 untreated3 Kal1                         932 untreated paired-end Secretory   
##  7 untreated4 Kal1                        1018 untreated paired-end Secretory   
##  8 treated1   Ant2                        2331 treated   single-re… Intracellul…
##  9 treated2   Ant2                        2478 treated   paired-end Intracellul…
## 10 treated3   Ant2                        2575 treated   paired-end Intracellul…
## # … with 494 more rows

Where mandatory column are sample, transcript, count (named as you wish)

Plot

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

• The horizontal, vertical relative column names (mandatory)
• The abundance column name (mandatory)
• The annotations column name(s)

pasilla %>%
    heatmap(
        .horizontal = sample,
        .vertical = symbol,
        .abundance = `count normalised adjusted`,
        annotation = c(condition, type),
        log_transform = TRUE
    )

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

pasilla %>%
    group_by(location) %>%
    heatmap(
        .horizontal = sample,
        .vertical = symbol,
        .abundance = `count normalised adjusted`,
        annotation = c(condition, type),
        log_transform = TRUE
    )