radiator: an R package for RADseq Data Exploration, Manipulation and Visualization

Most genomic analysis look for patterns and trends with various statistics. Bias, noise and outliers can have bounded influence on estimators and interfere with polymorphism discovery. Avoid bad data exploration and control the impact of filters on your downstream genetic analysis. Use radiator to: import, explore, manipulate, visualize, filter, impute and export your GBS/RADseq data.

radiator is designed and optimized for fast computations of diploid data using Genomic Data Structure GDS file format and data science packages in tidyverse. radiator handles VCF files with millions of SNPs and files of several GB.

Installation

To try out the dev version of radiator, copy/paste the code below:

if (!require("devtools")) install.packages("devtools")
devtools::install_github("thierrygosselin/radiator")
library(radiator)

Note

Some Windows OS and Linux OS recently experienced some problems during installations, linked to CRAN & Bioconductor tango problems:

  • If you’re experiencing problems with radiator installation see troubleshooting section and try the lines below.

  • Verify that installing radiator also installed the Bioconductor packages: gdsfmt and SeqArray with version >= 1.28.1.

devtools::package_info(pkgs = "SeqArray") # to verify version

# If manually installing SeqArray is necessary
install.packages("BiocManager")
BiocManager::install("SeqArray")

Web site with additional info: https://thierrygosselin.github.io/radiator/

Life cycle

radiator is maturing, but in order to make the package better, changes are inevitable. Experimental functions will change, argument names will change. Your codes and workflows might break from time to time until radiator is stable. Consequently, depending on your tolerance to change, radiator might not be for you. Avoid using radiator if you suffer from the Semmelweis reflex. Philosophy, major changes and deprecated functions/arguments are documented in life cycle section of functions.