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ggvariant

Publication-ready genomic variant plots in a few lines of R.

ggvariant fills a gap in the Bioconductor/CRAN ecosystem: a simple, ggplot2-native package that takes you directly from a VCF file or data frame to beautiful, customisable variant visualisations — without wrestling with complex APIs or writing 50-line wrangling scripts.

The gap this fills

ggvariant gives both wet-lab biologists and experienced bioinformaticians the same clean, ggplot2-idiomatic entry point.

Installation

# Install from GitHub (once released)
# install.packages("remotes")
remotes::install_github("josh45-source/ggvariant")

Quick start

library(ggvariant)

# 1. Load a VCF file
variants <- read_vcf("my_variants.vcf")

# 2. If you have a data frame instead (e.g. from Excel)
variants <- coerce_variants(my_df,
  chrom = "Chr", pos = "Position",
  ref   = "Ref", alt = "Alt",
  gene  = "Gene", sample = "SampleID"
)

Core plots

Lollipop plot — variants along a gene

plot_lollipop(variants, gene = "TP53")

Add protein domain annotations:

tp53_domains <- data.frame(
  name  = c("Transactivation", "DNA-binding", "Tetramerization"),
  start = c(1,   102, 323),
  end   = c(67,  292, 356)
)

plot_lollipop(variants, gene = "TP53", domains = tp53_domains)

Colour by sample instead of consequence:

plot_lollipop(variants, gene = "TP53", color_by = "sample")

Consequence summary

# Stacked bar by sample
plot_consequence_summary(variants)

# Proportional
plot_consequence_summary(variants, position = "fill")

# Top 10 mutated genes
plot_consequence_summary(variants, group_by = "gene", top_n = 10)

Mutational spectrum

# 6-class SBS spectrum
plot_variant_spectrum(variants)

# Faceted by sample
plot_variant_spectrum(variants, facet_by_sample = TRUE)

# Raw counts, not proportions
plot_variant_spectrum(variants, normalize = FALSE)

Interactive plots

All plot functions accept interactive = TRUE to return a plotly object for sharing with collaborators who don’t use R:

plot_lollipop(variants, gene = "BRCA1", interactive = TRUE)

Customisation

Because every function returns a standard ggplot object, you can layer on any ggplot2 or extension code:

library(ggplot2)

plot_lollipop(variants, gene = "KRAS") +
  scale_colour_brewer(palette = "Set2") +
  theme(legend.position = "bottom") +
  labs(subtitle = "KRAS mutations in cohort X")

Access palettes directly:

gv_palette("consequence")   # named hex vector
gv_palette("spectrum")      # COSMIC SBS colours

Design philosophy

• Minimal code — one function call per plot type
• Two entry points — VCF files and plain data frames
• ggplot2-native — every plot is a ggplot object; extend freely
• Opinionated defaults — looks good out of the box; no mandatory config
• Progressive disclosure — simple API for novices, full control for experts

Package structure

ggvariant/
├── R/
│   ├── ggvariant-package.R       # Package documentation
│   ├── read_vcf.R                # read_vcf() and coerce_variants()
│   ├── plot_lollipop.R           # plot_lollipop()
│   ├── plot_functions.R          # plot_consequence_summary(), plot_variant_spectrum()
│   └── utils.R                   # Theme, palettes, shared helpers
├── tests/
│   └── testthat/
│       └── test-core.R           # Unit tests
├── inst/
│   └── extdata/
│       └── example.vcf           # Bundled example VCF
├── DESCRIPTION
└── NAMESPACE

Roadmap

• plot_oncoprint() — sample × gene mutation matrix
• plot_copy_number() — CNV segment visualisation
• plot_rainfall() — kataegis / mutation density along genome
• plot_tmb() — tumour mutation burden comparison across cohorts
• BSgenome integration for automatic trinucleotide context extraction
• Shiny module for non-coding users

Contributing

Pull requests are welcome. Please open an issue first to discuss proposed changes. All contributions should include tests.

License

MIT

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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