sigvar

Quantify and visualize variability of mutational signatures within and across samples

Bioconductor version: 3.24 · Package version: 0.99.8

This package allows users to import mutational signature attribution (a.k.a. exposure) matrices and compute, visualize, and test their variabilities within and across samples.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("sigvar")

Details

Maintainer	Nicolas Alcala <alcalan@iarc.who.int>
Author	Maike Morrison [aut] (ORCID: <https://orcid.org/0000-0003-0430-1401>), Nicolas Alcala [aut, cre] (ORCID: <https://orcid.org/0000-0002-5961-5064>), Worldwide Cancer Research [fnd] (Grant 24-0106), French Ligue Nationale Contre le Cancer [fnd]
License	MIT + file LICENSE
URL	https://github.com/MaikeMorrison/sigvar
Bug Reports	https://github.com/MaikeMorrison/sigvar/issues
Source branch	devel
biocViews	DataImport, DriverMutation, Software, SomaticMutation, StatisticalMethod, StructuralVariation, Visualization, WholeGenome

Documentation

Introduction to Signature Variability Analysis with sigvar

Download

Follow the installation instructions to use this package in your R session.

Source package	sigvar_0.99.8.tar.gz
Windows binary (x86_64)	sigvar_0.99.8.zip
macOS binary (arm64)	sigvar_0.99.8.tgz
macOS binary (x86_64)	sigvar_0.99.8.tgz

Dependencies

Depends: R (>= 4.5)

Imports: dplyr, readr, ggplot2, rlang, tidyr, stringr, ggh4x, glue, ggtext, ggforce, scales, GenomicFeatures, GenomeInfoDb, BSgenome, Biostrings, rtracklayer, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, lifecycle, withr

Suggests: knitr, rmarkdown, testthat (>= 3.0.0), magick, BiocStyle, BSgenome.Hsapiens.UCSC.hg38, PNWColors, cowplot, ggpubr, ggrepel, kableExtra, lsa, patchwork, tidyverse