Tutorials
2  Update tidymass
3  Install docker version of tidymass
4  Build your own docker image based on tidymass
5  Create mass_dataset class from other tools
7  Convert raw data to mzXML/mgf
8  Raw data processing
9  Explore data
10  Data cleaning
11  Metabolite annotation
12  Statistical analysis
13  Pathway enrichment
14  Whole workflow using tidymass
15  Case study data
16  Case study for tidyMass
17  Repeat case study by docker
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0  Install tidymass

17   Repeat case study by docker

 

TidyMass packages:

Introduction

You can use the docker image of case study to repeat all the analysis and results in the tidymass manuscript.

The link of the docker image: https://hub.docker.com/r/jaspershen/tidymass-case-study

Download and install docker

Please refer to the official website to download and install docker. And then run docker.

  1. If you are using Mac, see here.

  2. If you are using Windows, see here.

  3. If you are using Linux, see here.

Pull the tidymass-case-study image

Open your terminal and then type the code below:

docker pull jaspershen/tidymass-case-study:latest

Run tidymass-case-study docker image

In your terminal, run the code below:

docker run -e PASSWORD=tidymass -p 8787:8787 jaspershen/tidymass-case-study:latest

Then open the browser and visit http://localhost:8787 to power on RStudio server. The user name is rstudio and the password is tidymass.

Run case study code

Open the tidymass_case_study.Rmd, and then click Knit.

Session information

sessionInfo()
#> R version 4.1.2 (2021-11-01)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur 10.16
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] magrittr_2.0.2
#> 
#> loaded via a namespace (and not attached):
#>  [1] bookdown_0.24   here_1.0.1      rprojroot_2.0.2 digest_0.6.29  
#>  [5] R6_2.5.1        jsonlite_1.7.3  evaluate_0.15   blogdown_1.7   
#>  [9] rlang_1.0.1     stringi_1.7.6   cli_3.2.0       rstudioapi_0.13
#> [13] jquerylib_0.1.4 bslib_0.3.1     rmarkdown_2.11  tools_4.1.2    
#> [17] stringr_1.4.0   xfun_0.29       yaml_2.3.4      fastmap_1.1.0  
#> [21] compiler_4.1.2  htmltools_0.5.2 knitr_1.37      sass_0.4.0
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