Create a heatmap plot of mutation subtype proportions. — plot_trinucleotide

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

# Plot the trinucleotide proportions per sample, facetted by dose group.
example_file <- system.file("extdata", "Example_files",
                            "example_mutation_data_filtered.rds",
                            package = "MutSeqR")
example_data <- readRDS(example_file)
# 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")
#> Performing internal depth correction to prevent double-counting...
#> Internal depth correction complete.
#> Joining with `by = join_by(normalized_context_with_mutation, sample)`
#> Joining with `by = join_by(sample, normalized_context)`
plot <- plot_trinucleotide_heatmap(mf_96,
                                  group_col = "sample",
                                  facet_col = "dose_group")
#> Cutting off at maximum mutation proportion value of 0.2