Split the heatmap row-wise depending on the biggest branches in the cladogram.

split_rows() from a `InputHeatmap` object, split the row cladogram.

split_columns() from a `InputHeatmap` object, split the column cladogram.

split_rows(.data, number_of_groups)

# S4 method for class 'InputHeatmap'
split_rows(.data, number_of_groups)

split_columns(.data, number_of_groups)

# S4 method for class 'InputHeatmap'
split_columns(.data, number_of_groups)

Source

[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)

Arguments

.data: A `InputHeatmap`
number_of_groups: An integer. The number of groups to split the cladogram into.

Value

A `InputHeatmap` object that gets evaluated to a `ComplexHeatmap`

Details

maturing

It uses `ComplexHeatmap` as visualisation tool.

maturing

It uses `ComplexHeatmap` as visualisation tool.

References

Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.

Examples



hm = 
  tidyHeatmap::N52 |>
  tidyHeatmap::heatmap(
    .row = symbol_ct,
    .column = UBR,
    .value = `read count normalised log`
)

hm |> split_rows(2)




hm = 
  tidyHeatmap::N52 |>
  tidyHeatmap::heatmap(
    .row = symbol_ct,
    .column = UBR,
    .value = `read count normalised log`
)

hm |> split_columns(2)