ggoncoplot

ggoncoplot creates interactive oncoplots from mutation level datasets

Installation

You can install the development version of ggoncoplot like so:

remotes::install_github('selkamand/ggoncoplot')

Usage

For complete usage, see manual

Input

The input for ggoncoplot is a data.frame with 1 row per mutation in cohort and columns describing the following:

• Gene Symbol

• Sample Identifier

• (optional) mutation type

• (optional) tooltip (character string: what we show on mouse hover over a particular mutation)

These columns can be in any order, and named anything. You define the mapping of your input dataset columns to the required features in the call to ggoncoplot

Basic Example

library(ggoncoplot)

# TCGA GBM dataset from TCGAmuations package
gbm_csv <- system.file(package='ggoncoplot', "testdata/GBM_tcgamutations_mc3_maf.csv.gz")
gbm_df <- read.csv(file = gbm_csv, header=TRUE)

gbm_df |> 
  ggoncoplot(
    col_genes = 'Hugo_Symbol', 
    col_samples = 'Tumor_Sample_Barcode', 
    col_mutation_type = 'Variant_Classification', 
    topn = 10, 
    interactive = FALSE
  )
#> 
#> ── 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

Add marginal plots

gbm_df |> 
  ggoncoplot(
    col_genes = 'Hugo_Symbol', 
    col_samples = 'Tumor_Sample_Barcode', 
    col_mutation_type = 'Variant_Classification', 
    topn = 10, 
    draw_gene_barplot = TRUE, 
    draw_tmb_barplot = TRUE,
    interactive = FALSE
  )
#> 
#> ── 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
#> ! TMB plot: Ignoring `col_mutation_type` since `log10_transform = TRUE`.
#> This is because you cannot accurately plot stacked bars on a logarithmic scale

Add clinical metadata

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

gbm_df |> 
  ggoncoplot(
   col_genes = "Hugo_Symbol",
   col_samples = "Tumor_Sample_Barcode",
   col_mutation_type = "Variant_Classification",
   metadata = gbm_clinical_df,
   cols_to_plot_metadata = c('gender', 'histological_type', 'prior_glioma', 'tumor_tissue_site'),
   draw_tmb_barplot = TRUE, 
   draw_gene_barplot = TRUE, 
   show_all_samples = TRUE,
   interactive = FALSE
  )
#> 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
#> ! TMB plot: Ignoring `col_mutation_type` since `log10_transform = TRUE`.
#> This is because you cannot accurately plot stacked bars on a logarithmic scale
#> 
#> ── Plotting Sample Metadata ────────────────────────────────────────────────────
#> ! Categorical columns must have <= 6 unique values to be visualised. Columns with too many unique values:  (20),  (388), and  (388)
#> 
#> ── Sorting
#> ℹ Sorting X axis by: Order of appearance
#> 
#> ── Generating Plot
#> ℹ Found 4 plottable columns in data

Acknowledgements

We acknowledge the developers and contributors whose packages and efforts were integral to the development of ggoncoplot:

• David Gohel for the ggiraph package, which enables the interactivity of ggoncoplot.
• Thomas Lin Pedersen for his contributions to the patchwork package and the maintenance of ggplot2.
• Hadley Wickham and all contributors to the ggplot2 package, which provides a robust foundation for data visualization in R.

Additionally, we thank Dr. Marion Mateos for her insightful feedback during the early stages of ggoncoplot development.

Community Contributions

All types of contributions are encouraged and valued. See our guide to community contributions for different ways to help.