This article surveys other R and Bioconductor tools that address
parts of the same problem space as ggvariant, and states
where ggvariant sits relative to each. None of the
alternatives here are deficient; each was built for a different point in
the workflow, and two of them predate ggvariant by roughly
a decade.
Reading VCF into R
vcfR (Knaus & Grünwald 2017) and VariantAnnotation (Obenchain et
al. 2014) both read VCF files into R and both predate
ggvariant by about ten years. vcfR represents a VCF as an
S4 vcfR object with base R plotting methods for
quality-control diagnostics such as coverage, depth, and quality
distributions; it is not built around ggplot2 and its
output is not a data frame. VariantAnnotation is part of the core
Bioconductor variant-calling infrastructure: it parses VCF into
VCF/CollapsedVCF objects built on
GRanges, designed to interoperate with the rest of
Bioconductor’s genomic-ranges ecosystem (annotation lookups, overlap
operations, reference genome access) rather than to produce plots
directly.
read_vcf() covers a narrower slice of the same job: it
returns a plain tidy data frame (a gvf object — a
data.frame with an added class attribute, nothing more),
trading the richer Bioconductor object model for a format any
dplyr/tidyr/base R workflow already knows how
to manipulate, and for direct compatibility with
ggvariant’s own plotting functions.
Cancer genomics summary plots
maftools (Mayakonda et al. 2018), GenVisR (Skidmore et al. 2016), and
ComplexHeatmap (Gu et al. 2016) all provide oncoprint/waterfall-style
summary plots, tracing back to the visualisation introduced by
cBioPortal (Gao et al. 2013; Cerami et al. 2012). maftools works from
MAF-format input and is tightly coupled to that cancer-genomics-specific
data model. ComplexHeatmap’s oncoPrint() is general-purpose
heatmap infrastructure that happens to support the oncoprint layout, at
the cost of a larger, more general API surface aimed at heatmaps in
general, not variant data specifically.
plot_oncoprint() (aliased plot_waterfall())
implements the same memo-sort gene/sample ordering these tools use, but
takes the same tidy gvf input as every other
ggvariant function and returns a plain ggplot
object, so it composes with ggplot2 layers the way the rest
of the package does.
Mutational signature analysis
MutationalPatterns (Blokzijl et al. 2018) computes and visualises
mutational signatures against the full 96-class trinucleotide context
(Alexandrov et al. 2020), including signature refitting against
reference catalogues, and depends on a BSgenome
reference-genome package to extract that context.
plot_variant_spectrum() does one narrower thing: it plots
the unweighted 6-class single-base-substitution spectrum with no genome
dependency. 96-class context support is planned but not yet implemented
(context/genome currently abort rather than
doing anything); until then, plot_variant_spectrum() is not
a substitute for MutationalPatterns’ signature-analysis capability, only
for the simpler 6-class summary.
Where ggvariant sits
ggvariant’s distinguishing feature is not that it does
more than these tools — several of them do substantially more. It is a
small API surface: read a VCF file or a plain data frame, get back a
gvf tidy data frame, call one function per plot type, get
back an ordinary ggplot object. There is no Bioconductor
dependency and no bespoke S4 class to learn. That trade-off is real:
less capability, in exchange for less to learn and full
ggplot2 composability.
References
- Knaus BJ, Grünwald NJ (2017). vcfR: a package to manipulate and visualize variant call format data in R. Molecular Ecology Resources, 17(1), 44-53. doi:10.1111/1755-0998.12549
- Obenchain V, Lawrence M, Carey V, Gogarten S, Shannon P, Morgan M (2014). VariantAnnotation: a Bioconductor package for exploration and annotation of genetic variants. Bioinformatics, 30(14), 2076-2078. doi:10.1093/bioinformatics/btu168
- Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP (2018). Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Research, 28(11), 1747-1756. doi:10.1101/gr.239244.118
- Skidmore ZL, Wagner AH, Lesurf R, Campbell KM, Kunisaki J, Griffith OL, Griffith M (2016). GenVisR: Genomic Visualizations in R. Bioinformatics, 32(19), 3012-3014. doi:10.1093/bioinformatics/btw325
- Gu Z, Eils R, Schlesner M (2016). Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics, 32(18), 2847-2849. doi:10.1093/bioinformatics/btw313
- Gao J, Aksoy BA, Dogrusoz U, et al. (2013). Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Science Signaling, 6(269), pl1. doi:10.1126/scisignal.2004088
- Cerami E, Gao J, Dogrusoz U, et al. (2012). The cBio Cancer Genomics Portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discovery, 2(5), 401-404. doi:10.1158/2159-8290.CD-12-0095
- Blokzijl F, Janssen R, van Boxtel R, Cuppen E (2018). MutationalPatterns: comprehensive genome-wide analysis of mutational processes. Genome Medicine, 10(1), 33. doi:10.1186/s13073-018-0539-0
- Alexandrov LB, Kim J, Haradhvala NJ, et al.; PCAWG Consortium (2020). The repertoire of mutational signatures in human cancer. Nature, 578(7793), 94-101. doi:10.1038/s41586-020-1943-3