1000 tools paper

This repository contains code and analysis for the "Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape" publication.

Directory structure

• R/ - Folder containing R code files. See documentation in files for more detail.
• output/ - Output files created by the workflow. These versions are stored here as a record but will be overwritten by running the workflow.
  • data-tables/ - Data files created by the workflow
  • figures/ - Figures shown in the paper
  • supplementary/ - Supplementary figures shown in the paper
  • tables/ - Tables shown in the paper
• .gitignore - Git configuration file
• 1000-tools.Rproj - RStudio project file
• LICENSE.md - License file
• README.md - This README file
• renv/ - Internal {renv} files
• renv.lock - {renv} lock file specifying R package dependencies

Other files created by set up or the workflow

• .Renviron - Local R configuration file, created as part of setting up
• _cache/ - Cache files created by the workflow to speed up some parts of the analysis
• _targets/ - Internal {targets} files

Setting up

R Dependencies

R package dependencies are managed using {renv}. They should be automatically installed when you start an R session inside the repository but to make sure run:

renv::restore()

Crossref

Information about publications and preprints is retrieved from the Crossref API using the {rcrossref} package. As explained in ?rcrossref::`rcrossref-package` Crossref provides faster access to people who give an email address. To do this add the following line to your .Renviron:

crossref_email=your@email.com

johnnydep

Package dependencies for PyPI tools are retrieved using the johnnydep tool (https://pypi.org/project/johnnydep/). For these stages to work you must have johnnydep installed. The easiest way to do that is using pip or conda:

pip install johnnydep
# OR
conda install johnnydep

Once johnnydep is installed find the path to it using which and set a JOHNNYDEP_PATH variable in your .Renviron.

which johnnydep

JOHNNYDEP_PATH=/path/to/your/johnnydep

Fonts

To make sure fonts used in plots are available follow these steps:

1. Download and install the Noto Sans and Noto Sans Maths fonts

   • For MacOS users the easiest way to do this is using Homebrew:

   brew install font-noto-sans font-noto-sans-math

   • Noto Sans is also available from Google Fonts

2. Import fonts into R by running extrafont::font_import()

If these fonts are not available the plots will still be produced they will just use the standard default font.

Analytics

This workflow can also generate analysis of usage of the scRNA-tools website but it requires access to the Google Analytics group so will need to be switched off for most people

No analytics access (most people)

Edit the _targets.R file and make sure the include_analytics variable is set to FALSE.

include_analytics <- FALSE

Analytics access

Data for plots showing usage statistics of the scRNA-tools website are collected using the {googleAnalyticsR} package. For this to work you must set up authentication with the googleAnalyticsR::ga_auth_setup() function following the instructions here https://code.markedmondson.me/googleAnalyticsR/articles/setup.html.

At the end of the process your .Renviron file should contain lines similar to these:

GAR_CLIENT_JSON=/path/to/oauth.json
GARGLE_EMAIL=your@email.com

Running analysis

The analysis workflow is managed using {targets}. Once set up is complete you can run the workflow using:

targets::tar_make()

Some of the steps (collecting reference and GitHub repository information) take a while to run. Once the workflow is complete various output files will be created in the output/ directory. If you want to view any of the intermediate parts of the workflow you can load the output of any target using:

targets::tar_load(target_name)

Updating analysis

The analysis is pinned to a particular date and version of the scRNA-tools database. If you want to repeat the analysis for a more recent version edit _targets.R and modify the date target:

tar_target(
    date,
    "YYYY-MM-DD"
)

License

The code is available under the MIT license.

Citation

If you use any of the code in this repository or analysis in the publication please cite:

Zappia L, Theis FJ. "Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape", Genome Biology (2021), DOI: 10.1186/s13059-021-02519-4

@ARTICLE{Zappia2021-bc,
  title    = "Over 1000 tools reveal trends in the single-cell {RNA-seq}
              analysis landscape",
  author   = "Zappia, Luke and Theis, Fabian J",
  journal  = "Genome Biol.",
  volume   =  22,
  number   =  1,
  pages    = "301",
  month    =  oct,
  year     =  2021,
  language = "en",
  doi      = "10.1186/s13059-021-02519-4",
  url      = "https://doi.org/10.1186/s13059-021-02519-4"
}