This is a hand-tutorial for learning the basic syntax for executing the RABIES software https://github.com/CoBrALab/RABIES. This tutorial was first developed for and presented at the INCF Neuroinformatics assembly 2023, and the original repository can be found here https://github.com/grandjeanlab/INCF_preclinical. The slides presented during the workshop (i.e. an introduction to each stage of the pipeline) are provided under RABIES_overview.pdf.
To follow this tutorial, your hardware must allow for running Docker (or Singularity/Apptainer), and you should have ~10Gb of free space to download the RABIES image.
- Install Docker following appropriate instructions for your operating system (available for Linux, Windows and Mac distributions). Alternatively, the same steps can be conducted using Singularity/Apptainer using a Linux system, although the execution syntax is slightly different (refer to the RABIES documentation).
- Pull the RABIES image using Docker, specifying a RABIES version tag:
docker pull gabdesgreg/rabies:0.4.8. This may take approximately 10-15 min. This tutorial was tested with RABIES version 0.4.7, 0.4.8 and 0.5.0, using Docker version 24.0.2 on a Linux platform. - Download this github repository. You can download the repo manually by clicking on "code -> download ZIP", or from a terminal using git with
git clone https://github.com/CoBrALab/RABIES_tutorial. - (OPTIONAL) Jupyter Notebook to open the notebooks. Alternatively, you can run operations directly from a terminal (the notebook itself is for demonstration purpose).
The tutorial_notebook.ipynb jupyter notebook regroups a series of shell commands which execute each stage of the pipeline on the token_dataset/ (fake dataset for testing purpose) you'll download from this repository. You can execute the notebook using Jupyter yourself, or open it on Github and reproduce each step in your terminal.
This repository also provides the quality control outputs generated from the test_dataset/ (i.e. real data), found in test_dataset_QC/, and the steps for reproducing these outputs are documented in the process_test_dataset.ipynb notebook.