Overview

cgmisc is an R package that enables enhanced data analysis and visualization of results from GWAS. The package contains several utilities and modules that complement and enhance the functionality of the existing software. It also provides several tools for advanced visualization of genomic data and utilizes 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.

Installation

The cgmisc package is available on GitHub and can be installed with the help of the devtools package:

install.packages('devtools')
require(devtools)
install_github('cgmisc-team/cgmisc')
require(cgmisc)

Now the package is ready to use. In the next chapter we will guide you through its main functionalities. The more detailed instructions can be found in the vignette provided with the package or downloaded here.

Data

Whenever possible, the cgmisc package works with data structures implemented and used by the GenABEL package. In particular, the gwaa.data-class and the gwaa.scan-class structures are used. The package is shipped with an example dataset called cgmisc_data. The example dataset contains genotyping data (Illumina CanineHD array, canFam2 assembly) for N=207 German shepherds originally collected for the project described in this study. However, to illustrate various features of the cgmisc package, the phenotypes included in the example dataset have been simulated. Use the following command to load the example dataset:

data('cgmisc_data')

Example analyses

In the vignette we provide the example GWAS analysis pipeline to demonstrate package capabilities in a series of practical examples based on a showcase data included in the package.

Bugs reporting

If you encounter a bug or you have any question regarding the package, do not hesitate to contact us! We are waiting for your feedback to make our package better!
Our team page
Google discussion group

Citing

Kierczak M, Jabłońska J, Forsberg SKG, Bianchi M, Tengvall K, Pettersson M, Scholz V, Meadows JRS, Jern P, Carlborg Ö, Lindblad-Toh K. cgmisc: enhanced genome-wide association analyses and visualization. Bioinformatics. Oxford University Press; 2015;31: 3830–3831. doi:10.1093/bioinformatics/btv426