Create a heatmap plot of mutation subtype proportions.

This function creates a heatmap plot of subtype proportions for a given grouping variable. The groups may be facetted by a second variable. Mutation sums for each facet group and normalized subtype are calculated and displayed.

Usage

plot_trinucleotide_heatmap(
  mf_data,
  group_col = "sample",
  facet_col = "dose",
  mf_type = "min",
  mut_proportion_scale = "turbo",
  max = 0.2,
  rescale_data = FALSE,
  condensed = FALSE
)

Arguments

mf_data

A data frame containing the mutation frequency data at the desired base resolution. This is obtained using the 'calculate_mf' with subtype_resolution set to the desired resolution. cols_to_group should be the same as 'group_col'.

group_col

The variable to group by.

facet_col

The variable to facet by.

mf_type

The type of mutation frequency to plot. Options are "min" or "max". (Default: "min")

mut_proportion_scale

The scale option for the mutation proportion. Options are passed to viridis::scale_fill_viridis_c. One of # inferno, magma, plasma, viridis, cividis, turbo, mako, or rocket. We highly reccomend the default for its ability to disciminate hard to see patterns. (Default: "turbo")

max

Maximum value used for plotting the proportions. Proportions that are higher will have the maximum colour. (Default: 0.2)

rescale_data

Logical value indicating whether to rescale the mutation proportions to increase the dynamic range of colors shown on the plot. (Default: TRUE)

condensed

More condensed plotting format. Default = FALSE.

Value

A ggplot object representing the heatmap plot.

Examples

if (requireNamespace("MutSeqRData", quietly = TRUE)) {
  # Plot the trinucleotide proportions per sample, facetted by dose group.

  # Example data consists of 24 mouse bone marrow DNA samples imported
  # using import_mut_data() and filtered with filter_mut as in Example 4.
  # Sequenced on TS Mouse Mutagenesis Panel. Example data is
  # retrieved from MutSeqRData, an ExperimentHub data package.
  library(ExperimentHub)
  eh <- ExperimentHub()
  example_data <- eh[["EH9861"]]

  # define dose_group order
  example_data$dose_group <- factor(example_data$dose_group,
    levels = c(
      "Control", "Low",
      "Medium", "High"
    )
  )
  mf_96 <- calculate_mf(example_data,
    cols_to_group = "sample",
    variant_types = "snv",
    subtype_resolution = "base_96",
    retain_metadata_cols = "dose_group"
  )
  plot <- plot_trinucleotide_heatmap(mf_96,
    group_col = "sample",
    facet_col = "dose_group"
  )
}