cgmisc is a R package that enables enhanced data analysis and visualisation of results from GWAS. The package contains several utilities and modules that complement and enhance the functionality of existing softwares. It also provides several tools for advanced visualisation of genomic data and utilises the power of the R language to aid in preparation of publication-quality figures. Some of the package functions are specific for the domestic dog (Canis familiaris) data.
Beginning from version 2.9.11, we are no longer using releases system. Instead, we maintain cgmisc in the CD/CI manner. From time to time, major versions will be frozen and available as source packages. Otherwise, track commit messages to know what has changed.
To ensure reproducibility of articles using cgmisc
, we provide a Docker container with working GenABEL
and pre-installed cgmisc
.
We have recently moved to ghcr.io and no longer maintain images on
DockerHub. To pull and run the container:
docker pull ghcr.io/cgmisc-team/cgmisc:release
cgmisc
enchances functionalities of GenABEL
package which is, unfortunately, no longer supported. Thus you will need to install it manualy from source available on CRAN Package Archives and, in addition, you need to be advised that GenABEL won't compile for r-base > 4.1.3! Thus we strongly recommend to go for the Docker container solution:
-
install.packages("https://cran.r-project.org/src/contrib/Archive/GenABEL.data/GenABEL.data_1.0.0.tar.gz", type='source', repos=NULL)
-
install.packages("https://cran.r-project.org/src/contrib/Archive/GenABEL/GenABEL_1.8-0.tar.gz", type='source', repos=NULL)
In addition, some more packages are required, but they should be installed automatically.
We recommend to use an excellent renv
package to recreate optimal environment for cgmisc
installation. First, retrieve the renv.lock
file:
wget https://raw.githubusercontent.com/cgmisc-team/cgmisc/master/renv.lock
and put it in your project directory. Next, type this in R:
install.packages(renv)
library(renv)
renv::init()
Otherwise, we recommend installing cgmisc by using:
devtools::install_github('cgmisc-team/cgmisc')
To install using the tarball, open a terminal and type:
R CMD INSTALL cgmisc_[version].tar.gz
Kierczak M, Jablonska J, Forsberg SKG, Bianchi M, Tengvall K, Pettersson M, Scholz V, Meadows JRS, Jern P, Carlborg O Lindblad-Toh K. cgmisc: enhanced genome-wide association analyses and visualization. Bioinformatics. Oxford University Press; 2015;31: 3830-3831. doi:10.1093/bioinformatics/btv426
Here we list some publications where cgmisc has been helpful: