pretty_circular_mutation_frequency_heatmap

Usage

pretty_circular_mutation_frequency_heatmap(
  prettyOncoplot_output,
  cn_status_matrix,
  collated_results,
  these_samples_metadata,
  genes,
  cluster = T,
  keep_these_pathologies,
  min_sample_num = 20,
  col_fun,
  col_theme,
  return_data = FALSE,
  dend_location = "inside",
  clustering_distance_method = "euclidean",
  border = T,
  split_by_type = FALSE,
  rotate_degrees = 0,
  gap.degree = 15,
  show.sector.labels = FALSE,
  label_cex = 0.5,
  rownames_cex = 0.5,
  include_legend = F,
  colour_labels = F,
  label_group = "text",
  label_alpha
)

Arguments

prettyOncoplot_output: The output of the prettyOncoplot function
cn_status_matrix: The output of get_cn_states
collated_results: A list of data frames with sample_id as rownames and features as column names
these_samples_metadata: A data frame with metadata. Usually the output of [GAMBLR.results::get_gambl_metadata].
genes: A vector of genes to label
cluster: Whether to perform clustering. Default is TRUE (clustering is performed).
keep_these_pathologies: A vector of pathology values to show in the plot. All the remaining rows will be ignored.
min_sample_num: Minimum number of samples in a pathology to be considered for the plot. Pathologies with less than this number will be excluded. (20)
col_fun: Color function to modify the default color pallette of the heatmap.
col_theme: Alternatively, provide the color theme instead of `col_fun` to change the default colors of the heatmap.
return_data: Conditionally return the formatted data used for the plotting. Default is FALSE (only image is plotted and no data is returned).
dend_location: Location of the dendrogram. Default is "inside".
clustering_distance_method: Clustering method. Default is "euclidean".
border: Whether to draw border around heatmap. Default is TRUE (with border).
split_by_type: Whether to split the mutations by type. Default is FALSE (no splitting).
rotate_degrees: Rotate labels. Default is 0 (no rotation).
gap.degree: Gap degree. Default is 15.
show.sector.labels: Show labels for each sector of the heatmap. Default is FALSE (no labels).
label_cex: Number indicating the amount by which plotting text and symbols should be scaled relative to the default when displaying the labels. Default is 0.5.
rownames_cex: Number indicating the amount by which plotting text and symbols should be scaled relative to the default when displaying the rownames. Default is 0.5.
include_legend: Whether to include the legend. Default is FALSE (no legend).
colour_labels: Optionally color labels. Default is FALSE (no coloring).
label_group: How to group the labels. Default is "text".
label_alpha: Value from 0 to 1 to control alpha of the label.

Value

Nothing or a list of data frames (when return_data = TRUE)

Examples


library(dplyr)
library(GAMBLR.open)
suppressMessages(
  suppressWarnings({

metadata <- get_gambl_metadata() %>%
   dplyr::filter(!seq_type == "mrna") %>%
   dplyr::filter(pathology %in% names(get_gambl_colours("pathology"))) %>%
   check_and_clean_metadata(.,duplicate_action="keep_first")

all_coding <- get_coding_ssm(these_samples_metadata = metadata)

genes <- lymphoma_genes %>%
    dplyr::filter(DLBCL|FL|BL) %>%
    dplyr::pull(Gene) %>%
    unique %>%
    sort

oncoplot_output <- prettyOncoplot(
    all_coding,
    genes = genes,
    minMutationPercent = 2,
    these_samples_metadata = metadata,
    simplify_annotation = TRUE,
    return_inputs = TRUE
)

# Basic plot
pretty_circular_mutation_frequency_heatmap(
    prettyOncoplot_output = oncoplot_output,
    keep_these_pathologies = c(
        "FL", "DLBCL", "PMBCL", "BL", "HGBL"
    )
)
})) 

suppressMessages(
  suppressWarnings({
# Add sv layer
all_sv <- get_manta_sv(these_samples_metadata = metadata)
annotated_sv <- annotate_sv(all_sv) %>%
    dplyr::filter(gene %in% genes, !is.na(partner)) %>%
    dplyr::select(sample_id = tumour_sample_id, gene)

# This is to replicate the output format of collate_sv
sv_collated <- annotated_sv %>%
    dplyr::mutate(
        gene = paste("manta", gene, "sv", sep = "_"),
        mutated = "POS"
    ) %>%
    dplyr::distinct() %>%
    tidyr::pivot_wider(
        names_from = gene,
        values_from = mutated
    ) %>%
    replace(is.na(.), "NEG")

# Plot SSM + SVs
pretty_circular_mutation_frequency_heatmap(
    collated_results = list(sv_collated),
    prettyOncoplot_output = oncoplot_output,
    these_samples_metadata = metadata
)
})) 


suppressMessages(
  suppressWarnings({
regions_bed = GAMBLR.utils::create_bed_data(GAMBLR.data::grch37_ashm_regions,
                              fix_names = "concat",
                              concat_cols =c("gene","region"),
                              sep="-")
# Add aSHM data
ashm_freq <- get_ashm_count_matrix(
    these_samples_metadata = metadata,
    regions_bed = regions_bed,
    this_seq_type = "genome",
    projection = "grch37"
)

ashm_freq_collated <- mutate(ashm_freq,across(,~ifelse(.x>0,1,0)))

ashm_freq_collated <- ashm_freq_collated[,colSums(ashm_freq_collated) >130]
ashm_freq_collated <- tibble::rownames_to_column(ashm_freq_collated,
                                                 "sample_id")

# Comprehensive plot with SSM + SV + aSHM and some non-default arguments
pretty_circular_mutation_frequency_heatmap(
    collated_results = list(sv_collated, ashm_freq_collated),
    prettyOncoplot_output = oncoplot_output,
    these_samples_metadata = metadata,
    keep_these_pathologies = c("DLBCL", "FL", "BL"),
    split_by_type = TRUE,
    colour_labels = TRUE,
    label_cex = 0.4,
    rownames_cex = 0.4,
    include_legend = TRUE
)

}))