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 (Phaseolus vulgaris) in the United States. PeerJ 5:e4152 https://doi.org/10.7717/peerj.4152
Poster presented at the 2018 National Sclerotinia Initiative: https://doi.org/10.6084/m9.figshare.5791713
Kamvar, Z. N., Amaradasa, B. S., Jhala, R., McCoy, S., Steadman, J. R., & Everhart, S. E. (2017, November). Data and analysis for population structure and phenotypic variation of Sclerotinia sclerotiorum from dry bean (Phaseolus vulgaris) in the United States. https://doi.org/10.17605/OSF.IO/K8WTM
Note: This citation represents the data and code that was in this repository at the time of re-submission (2017-11-15 20:42 UTC), For the docker container, this represents tag v1.1. You can find the live code at https://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
- compare-aldrich-wolfe.md
The analysis is controlled via two docker containers:
- sclerotinia-366-dependencies: defines the complete software environment used, built on top of the rocker/verse:3.4.2 container. (See the Dockerfile)
- sclerotinia-366 is built on top of the above container and contains the results of the analysis. (See the Dockerfile)
The sclerotinia-366-dependencies container is regularly rebuilt on docker hub whenever rocker/verse:3.4.2 updates and the sclerotinia-366 container is rebuilt on CircleCI whenever the git repository is updated.
As of this writing, the containers are up to date with R version 3.4.2 and packages downloaded from the MRAN snapshot on 2017-10-31.
Jump to Reproduction via Docker or Reproduction Locally.
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 ~3.21Gb, 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/everhartlab/sclerotinia-366/
To run the docker container, make sure you have Docker running, open your terminal and type:
docker run --name ssc --rm -dp 8787:8787 -e ROOT=TRUE everhartlab/sclerotinia-366:latestThis will first check to make sure you have the container on your machine. If you don't, Docker will automatically download it for you. It will spin up the Docker container on your machine, give it the name "ssc", 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 following credentials:
- username: rstudio
- 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. Don't forget to stop the container when you are finished:
docker stop sscIf you don't want to pull from docker hub, you can build the container locally. First, download the repository
git clone https://github.com/everhartlab/sclerotinia-366.git
cd sclerotinia-366/
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
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.
Note: This analysis is only guaranteed to work with the stated software environment.
Required software:
- GNU Make (If you're on Windows, you can use MinGW: http://www.mingw.org/)
- R (version 3.4.1 or greater)
- LaTeX
- pandoc (Note: pandoc ships with Rstudio)
- devtools
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()
#> Session info --------------------------------------------------------------------------------------
#> setting value
#> version R version 3.4.2 (2017-09-28)
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> tz UTC
#> date 2018-04-12
#> Packages ------------------------------------------------------------------------------------------
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