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ggoncoplot

Source: R/oncoplot.R
ggoncoplot.Rd

Creates an interactive oncoplot to visualize the mutation landscape of cancer cohorts.

Usage

ggoncoplot(
  data,
  col_genes,
  col_samples,
  col_mutation_type = NULL,
  genes_to_include = NULL,
  genes_to_ignore = NULL,
  col_tooltip = col_samples,
  topn = 10,
  return_extra_genes_if_tied = FALSE,
  draw_gene_barplot = FALSE,
  draw_tmb_barplot = FALSE,
  copy = c("sample", "gene", "tooltip", "mutation_type", "nothing"),
  palette = NULL,
  metadata = NULL,
  metadata_palette = NULL,
  col_samples_metadata = col_samples,
  cols_to_plot_metadata = NULL,
  metadata_require_mutations = TRUE,
  pathway = NULL,
  col_genes_pathway = col_genes,
  show_all_samples = FALSE,
  total_samples = c("any_mutations", "all", "oncoplot"),
  sample_order = NULL,
  interactive = TRUE,
  options = ggoncoplot_options(),
  verbose = TRUE
)

Arguments

data

data for oncoplot. A data.frame with 1 row per mutation in your cohort. Must contain columns describing gene_symbols and sample_identifiers (data.frame)

col_genes

name of data column containing gene names/symbols (string)

col_samples

name of data column containing sample identifiers (string)

col_mutation_type

name of data column describing mutation types (string, optional)

genes_to_include

specific genes to include in the oncoplot (character, optional)

genes_to_ignore

names of the genes that should be ignored (character, optional)

col_tooltip

name of data column containing whatever information you want to display in (string, defaults to col_samples)

topn

how many of the top genes to visualize. Ignored if genes_to_include is supplied (number, default 10)

return_extra_genes_if_tied

instead of strictly returning topn genes, in the case of ties (where multiple genes are mutated in the exact same number of samples, complicating selection of top n genes), return all tied genes (potentially more than topn). If FALSE, will return strictly topn genes, breaking ties based on order of appearance in dataset (flag, default FALSE)

draw_gene_barplot

add a barplot describing number of samples with each gene mutated (right side) (flag, default FALSE)

draw_tmb_barplot

add a barplot describing total number of mutations in each sample (above main plot). If a single gene is mutated multiple times, all mutations are counted towards total (flag, default FALSE)

copy

value to copy to clipboard when an oncoplot tile is clicked (string, one of 'sample', 'gene', 'tooltip', 'mutation_type', 'nothing', default 'sample')

palette

a named vector mapping all possible mutation types (vector names) to colors (vector values, optional)

metadata

dataframe describing sample level metadata. One column must contain unique sample identifiers. Other columns can describe numeric / categorical metadata (data.frame, optional)

metadata_palette

A list of named vectors. List names correspond to metadata column names (categorical only). Vector names to levels of columns. Vector values are colors, the vector names are used to map values in data to a color. (optional)

col_samples_metadata

which column in metadata data.frame describes sample identifiers (string, defaults to col_samples)

cols_to_plot_metadata

names of columns in metadata that should be plotted (character, optional)

metadata_require_mutations

filter out samples from metadata lacking any mutations in data (flag, default TRUE)

pathway

a two column dataframe describing pathway. The column containing gene names should have the same name as col_gene (data.frame, optional)

col_genes_pathway

which column in pathway data.frame describes gene names (string, defaults to col_genes)

show_all_samples

show all samples in oncoplot, even if they don't have mutations in the selected genes. Samples only described in metadata but with no mutations at all are still filtered out by default, but you can show these too by setting metadata_require_mutations = FALSE (flag, default FALSE)

total_samples

Strategy for calculating the total number of samples. This value is used to compute the proportion of mutation recurrence displayed in the tooltip when hovering over the gene barplot, or as a text annotation when ggoncoplot_options(show_genebar_labels = TRUE) is set to TRUE. Possible values:

  • any_mutations: All the samples that are in data (the mutation dataset), irrespective of whether they are on the oncoplot or not.

  • oncoplot: Only the samples that are present on the oncoplot.

  • all: All the samples in either data or metadata.

sample_order

sample IDs in the order they should be shown on oncoplot (left to right). Overrides gene-based auto-ranking. (character vector).

interactive

should plot be interactive (boolean, default TRUE)

options

a list of additional visual parameters created by calling ggoncoplot_options(). See ggoncoplot_options for details.

verbose

verbose mode (flag, default TRUE)

Value

ggplot or girafe object if interactive=TRUE

Details

This function generates a customizable oncoplot that displays the most frequently mutated genes (default top 10) along with interactive tooltips and clickable elements.

Examples

# ===== GBM =====
gbm_csv <- system.file(
  package = "ggoncoplot",
  "testdata/GBM_tcgamutations_mc3_maf.csv.gz"
)

gbm_clinical_csv <- system.file(
  package = "ggoncoplot",
  "testdata/GBM_tcgamutations_mc3_clinical.csv"
)

gbm_df <- read.csv(file = gbm_csv, header = TRUE)
gbm_clinical_df <- read.csv(file = gbm_clinical_csv, header = TRUE)

# Plot Basic Oncoplot
ggoncoplot(
  gbm_df,
  "Hugo_Symbol",
  "Tumor_Sample_Barcode",
  col_mutation_type = "Variant_Classification",
  metadata = gbm_clinical_df,
  cols_to_plot_metadata = "gender"
)
#> ℹ 2 samples with metadata have no mutations. Fitering these out
#> ℹ To keep these samples, set `metadata_require_mutations = FALSE`. To view them in the oncoplot ensure you additionally set `show_all_samples = TRUE`
#> → TCGA-06-0165-01
#> → TCGA-06-0167-01
#> 
#> ── Identify Class ──
#> 
#> ℹ Found 7 unique mutation types in input set
#> ℹ 0/7 mutation types were valid PAVE terms
#> ℹ 0/7 mutation types were valid SO terms
#> ℹ 7/7 mutation types were valid MAF terms
#> ✔ Mutation Types are described using valid MAF terms ... using MAF palete
#> 
#> ── Plotting Sample Metadata ────────────────────────────────────────────────────
#> 
#> ── Sorting 
#> ℹ Sorting X axis by: Order of appearance
#> 
#> ── Generating Plot 
#> ℹ Found 1 plottable columns in data



# Customise how the Oncoplot looks
ggoncoplot(
  gbm_df,
  "Hugo_Symbol",
  "Tumor_Sample_Barcode",
  col_mutation_type = "Variant_Classification",
  metadata = gbm_clinical_df,
  cols_to_plot_metadata = "gender",

  # Customise Visual Options
  options = ggoncoplot_options(
    xlab_title = "Glioblastoma Samples",
    ylab_title = "Top 10 mutated genes"
  )
)
#> ℹ 2 samples with metadata have no mutations. Fitering these out
#> ℹ To keep these samples, set `metadata_require_mutations = FALSE`. To view them in the oncoplot ensure you additionally set `show_all_samples = TRUE`
#> → TCGA-06-0165-01
#> → TCGA-06-0167-01
#> 
#> ── Identify Class ──
#> 
#> ℹ Found 7 unique mutation types in input set
#> ℹ 0/7 mutation types were valid PAVE terms
#> ℹ 0/7 mutation types were valid SO terms
#> ℹ 7/7 mutation types were valid MAF terms
#> ✔ Mutation Types are described using valid MAF terms ... using MAF palete
#> 
#> ── Plotting Sample Metadata ────────────────────────────────────────────────────
#> 
#> ── Sorting 
#> ℹ Sorting X axis by: Order of appearance
#> 
#> ── Generating Plot 
#> ℹ Found 1 plottable columns in data