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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.

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 (FASTQ files and a metadata file)

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. Using 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

In the chiron_readataworkflow branch of the github repository, we provide the code and setup of a real data analysis with the ARMOR workflow. You can find it here. This example uses conda to manage the required software and R, including the installation of all R packages. More details can be found in the paper TODO: add link to preprint/paper.

For a detailed explanation of how to set up the config.yaml file and how to run the workflow, please see the "Using the ARMOR workflow" section above.