Last updated: 2021-01-11

Checks: 7 0

Knit directory: cytomapper_publication/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20200602)

The command set.seed(20200602) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: d0c4ccb

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version d0c4ccb. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .Rproj.user/

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/index.Rmd) and HTML (docs/index.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd d0c4ccb nilseling 2021-01-11 Fixed list
Rmd 4ba0377 nilseling 2021-01-11 Updated index file
html 950f541 Nils Eling 2020-11-18 Recompiled scripts
Rmd 08f5f0d Nils Eling 2020-11-18 Split figures into main and supplements
html 2684af4 Nils Eling 2020-09-22 Added htmls after commiting .Rmds
Rmd 3c9f49b Nils Eling 2020-09-22 Recompiled from new docker
html 3c9f49b Nils Eling 2020-09-22 Recompiled from new docker
Rmd 6f99bc3 nilseling 2020-09-22 Docker version bump
html 05d2b29 Nils Eling 2020-09-14 Recompiled htmls
html 8e6633e Nils Eling 2020-08-21 Recompiled scripts
Rmd 96a3874 nilseling 2020-08-21 Added installation instructions to website
html 9ddf2b3 Nils Eling 2020-08-20 Recompiled from with docker container
Rmd 5ff8ee7 nilseling 2020-07-22 Added info to website
html 5ff8ee7 nilseling 2020-07-22 Added info to website
html d47d609 nilseling 2020-07-22 Recompiled from docker
html 1dd9345 nilseling 2020-07-10 Recompiled index
html 2e3df83 nilseling 2020-07-09 Added Github icon and recompiled
html 5476a61 nilseling 2020-07-08 Recompiled files
html 2fbf302 nilseling 2020-06-02 Re-compiled htmls
html 7960caa nilseling 2020-06-02 Compiled htmls of preprocessing steps
Rmd 12bdedf nilseling 2020-06-02 Start workflowr project.

The scripts hosted on this website serve the purpose of testing, validating and publishing the cytomapper package.

Setting up the environment

For reproducibility purposes, we use Docker and workflowr to organize the scripts and the computational environment. Please follow these steps to set-up and run the analysis presented on this website:

  1. Install Docker
  2. Pull the docker image
docker pull nilseling/bioconductor_cytomapper:0.0.3
  1. Run the docker image
docker run -e PASSWORD=bioc -p 8787:8787 nilseling/bioconductor_cytomapper:0.0.3

Here, the set PASSWORD is bioc. This will be used to login to RStudio later.

  1. Open a browser window at http://localhost:8787/
  2. Sign in to RStudio using Username: rstudio and Password: bioc

You have now a running instance of all the software needed to reproduce the analysis.

Running the code

The following steps will guide you through running the analysis:

  1. Within RStudio, navigate to cytmapper_publication
  2. By clicking cytomapper_publication.Rproj, open the correct R project
  3. Navigate to analysis and run the scripts in the provided order

Further instructions can be found in the individual scripts.

Installing cytomapper

The cytomapper version for the Bioinformatics publication can be installed via:

install.packages(c("devtools", "workflowr", "tidyverse"))
devtools::install_github("BodenmillerGroup/cytomapper@v1.2.0")

The cytomapper version used for the bioRxiv submission can be installed via:

install.packages(c("devtools", "workflowr", "tidyverse"))
devtools::install_github("BodenmillerGroup/cytomapper@v1.1.2")

The Bioconductor release version of cytomapper can be obtained from Bioconductor. The following code will also install additional packages needed to perform the analysis.

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install(c("cytomapper", "workflowr", "tidyverse"))

The Bioconductor development version of cytomapper can be installed via:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install(version = "devel", update = TRUE, ask = FALSE)

BiocManager::install(c("cytomapper", "workflowr", "tidyverse"))

Data

The example dataset used for this analysis has been published in: https://www.cell.com/cell-metabolism/fulltext/S1550-4131(18)30691-0

The dataset is available for download from Mendeley Data: http://dx.doi.org/10.17632/cydmwsfztj.2

Specifically, the following files are used in the current analysis:

Furthermore, the presented data has been deposited on ExperimentHub an can be accessed using the imcdatasets package.

Citation

Please cite cytomapper as:

Nils Eling, Nicolas Damond, Tobias Hoch, Bernd Bodenmiller (2020). cytomapper: an R/Bioconductor package for visualisation of highly
  multiplexed imaging data. Bioinformatics, https://doi.org/10.1093/bioinformatics/btaa1061

sessionInfo()
R version 4.0.3 Patched (2020-11-08 r79411)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/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] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5      rstudioapi_0.13 whisker_0.4     knitr_1.30     
 [5] magrittr_2.0.1  R6_2.5.0        rlang_0.4.9     stringr_1.4.0  
 [9] tools_4.0.3     xfun_0.19       git2r_0.27.1    htmltools_0.5.0
[13] ellipsis_0.3.1  rprojroot_2.0.2 yaml_2.2.1      digest_0.6.27  
[17] tibble_3.0.4    lifecycle_0.2.0 crayon_1.3.4    later_1.1.0.1  
[21] vctrs_0.3.5     promises_1.1.1  fs_1.5.0        glue_1.4.2     
[25] evaluate_0.14   rmarkdown_2.5   stringi_1.5.3   compiler_4.0.3 
[29] pillar_1.4.7    httpuv_1.5.4    pkgconfig_2.0.3