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Charlotte Soneson edited this page Mar 18, 2019 · 43 revisions

ARMOR workflow

This is a graph of the ARMOR (Automated Reproducible MOdular RNA-seq) workflow. It shows all the steps in the workflow: Blue circles are rules run in R, orange circles from software called as shell commands. Dashed lines and light-colored circles are optional rules, controlled in config.yaml.

Requirements

ARMOR requires snakemake and (optionally but recommended) conda. See the snakemake manual and the conda user guide for help with the installation.

Quick start

Assuming that snakemake and conda are installed (see above), here is how to quickly get started. Simply download and run ARMOR on the example dataset with following commands:

git clone https://github.com/csoneson/ARMOR.git
cd ARMOR && snakemake --use-conda

This will clone the ARMOR repository to your computer, create conda environments with the required software and R packages installed and run the workflow on the small example dataset that we provide.

Note: The installation of the required software in the conda environments will take some time! And, this will likely not work on Mac OS X unless your system is setup with the necessary libraries to compile R packages; see Managing software

Using the ARMOR workflow

To use the ARMOR workflow on your own data, follow the steps outlined below carefully.

  1. Clone this repository to your local machine and set the working directory to the cloned repository:

    git clone https://github.com/csoneson/ARMOR.git
    cd ARMOR
    
  2. Prepare the input files

  3. Prepare the configuration file (config.yaml)

  4. Make sure that all the necessary software is available. This can be done in three ways:

    1. Use conda to install software (recommended)
    2. Set up and activate a conda environment
    3. Install software manually
  5. Set up the proper experimental design and contrast(s) for differential expression analysis

  6. Run the analysis

  7. Visualize results with iSEE

Testing the setup

We provide a small example data set that you can use to test your setup: See Testing the setup.

Example of a real data analysis

If you want to see a real data analysis with the ARMOR workflow, please have a look at the chiron_readataworkflow branch of the ARMOR GitHub repository. You can find it here. This example uses conda to manage the required software, including R and the installation of all R packages. More details can be found in the "Real data walk-through" section of our preprint:

S Orjuela*, R Huang*, KM Hembach*, MD Robinson, C Soneson: ARMOR: an Automated Reproducible MOdular workflow for preprocessing and differential analysis of RNA-seq data. bioRxiv doi: https://doi.org/10.1101/575951

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