Last updated: 2024-01-01

Checks: 2 0

Knit directory: mage_2020_marker-gene-benchmarking/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


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.

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 2632193. 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:    .Renviron
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    .snakemake/
    Ignored:    NSForest/.Rhistory
    Ignored:    NSForest/NS-Forest_v3_Extended_Binary_Markers_Supplmental.csv
    Ignored:    NSForest/NS-Forest_v3_Full_Results.csv
    Ignored:    NSForest/NSForest3_medianValues.csv
    Ignored:    NSForest/NSForest_v3_Final_Result.csv
    Ignored:    NSForest/__pycache__/
    Ignored:    NSForest/data/
    Ignored:    RankCorr/picturedRocks/__pycache__/
    Ignored:    benchmarks/
    Ignored:    config/
    Ignored:    data/cellmarker/
    Ignored:    data/downloaded_data/
    Ignored:    data/expert_annotations/
    Ignored:    data/expert_mgs/
    Ignored:    data/raw_data/
    Ignored:    data/real_data/
    Ignored:    data/sim_data/
    Ignored:    data/sim_mgs/
    Ignored:    data/special_real_data/
    Ignored:    figures/
    Ignored:    logs/
    Ignored:    results/
    Ignored:    weights/

Unstaged changes:
    Deleted:    analysis/expert-mgs-direction.Rmd
    Modified:   smash-fork

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/about.Rmd) and HTML (public/about.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
html fcecf65 Jeffrey Pullin 2022-09-09 Build site.
html af96b34 Jeffrey Pullin 2022-08-30 Build site.
html 0e47874 Jeffrey Pullin 2022-05-04 Build site.
html b5045c1 Jeffrey Pullin 2022-05-02 Build site.
Rmd 048156f Jeffrey Pullin 2022-05-02 Tweak analysis outputs
html 048156f Jeffrey Pullin 2022-05-02 Tweak analysis outputs
html 8b989e1 Jeffrey Pullin 2022-05-02 Build site.
html 0548273 Jeffrey Pullin 2022-05-02 Build site.
html 50bca7c Jeffrey Pullin 2022-05-02 workflowr::wflow_publish(all = TRUE, republish = TRUE)
html ca82ce0 Jeffrey Pullin 2021-02-16 Build site.
html 2863555 Jeffrey Pullin 2021-02-10 Build site.
Rmd 1ad9d6d Jeffrey Pullin 2021-02-10 Add workflowr website
Rmd 7971ea2 Jeffrey Pullin 2020-10-25 Initial commit

Reproducibility information

The code underlying this project is complex and running it is computationally intensive. Despite this, the project should be reproducible and even extendable with minimal effort. Find below a description of the key steps needed to reproduce the results. In the near future this process will be substantially streamlined.

  1. R package download: run the script code/install-r-package-deps.R
  2. Python package download: create and activate the conda environment envs/conda.yaml
  3. Dataset download: run the script code/download-raw-data.R
  4. Run the snakemake workflow: run bash run.sh at the command line
  5. Generate the workflowr website: run workflowr::wflow_build(files = "analysis/*)" at an R prompt.