This repository contains data, code, and a manuscript for analysis of 366 isolates of Sclerotinia sclerotiorum from the US and various countries around the world.
Kamvar ZN, Amaradasa BS, Jhala R, McCoy S, Steadman JR, Everhart SE. (2017) Population structure and phenotypic variation of Sclerotinia sclerotiorum from dry bean in the United States. PeerJ Preprints 5:e3311v1 https://doi.org/10.7287/peerj.preprints.3311v1
Kamvar, Z. N., Amaradasa, B. S., Jhala, R., McCoy, S., Steadman, J., & Everhart, S. E. (2017, October 3). Population structure and phenotypic variation of Sclerotinia sclerotiorum from dry bean in the United States. https://doi.org/10.17605/OSF.IO/EJB5Y
The analyses are arranged in the following order according to the Makefile:
- table-1.md
- MCG-virulence.md
- locus-stats.md
- MLG-distribution.md
- mlg-mcg.md
- RDA-analysis.md
- pop-diff.md
- tree.md
- wmn-differentiation.md
- by-year.md
This project is controlled via a Makefile which means that everything (analyses, tables, figures, the paper itself) is controlled via one command:
make
This will bootstrap the installation (warning: it will update packages), process the data, perform the analyses, and compile the paper.
Required software:
- GNU Make (If you're on Windows, you can use MinGW: https://www.mingw.org/)
- R (version 3.4.1 or greater)
- LaTeX
- pandoc (Note: pandoc ships with Rstudio)
- devtools
This repository contains a Dockerfile, which specifies the instructions to build a docker container. This is designed to capture the complete development environment of the analysis so that it can be accurately reproduced. The image is ~2.71Gb, so be sure that you have enough memory on your computer to run it.
To Install Docker, go here: https://docs.docker.com/engine/installation/#desktop. Once you have downloaded docker, you can either pull the container or build it. Pulling is by far the quickest way to do this. The docker container is located at https://hub.docker.com/r/zkamvar/sclerotinia-366/
Note: both ways assume that you are in the analysis directory
docker run --rm -dp 8787:8787 zkamvar/sclerotinia-366:latest
This will spin up the Docker container on your machine and expose it to port 8787. You can open your browser and type localhost:8787, and an instance of Rstudio server will appear. Sign in with the username: rstudio and password: rstudio.
Since the files in /analysis are write-protected, if you wanted to explore, you should copy the directory to your current working space:
- in the R console type:
system("cp -R /analysis ."). - open
/analysisand double click on znk_analysis.Rproj
From here you can re-run the analyses to your heart's content.
If you don't want to pull from docker hub, you can build the container.
docker build -t sclerotinia-366 .Now that things are built, you can run the analysis in the image with:
docker run -it sclerotinia-366 bash
Once you are in the container, you can run the analysis, which is mapped to analysis/. The make clean command will wipe out all derivative files and the make command will generate everything. Note that this took almost 2 hours to run on my machine due to several bootstrapping processes.
cd analysis/
make clean
make
options(width = 100)
imports <- packageDescription("WorldSclerotinia")$Imports
imports <- strsplit(imports, "[^A-z]*,\n")[[1]]
for (i in imports) suppressPackageStartupMessages(library(i, character.only = TRUE))
devtools::session_info()
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