clinical-atlasreg

--- DOCS UNDER CONSTRUCTION ---

Inputs:

• participants.tsv with target subject IDs
• bids folder
  • other folder containing bids-like processed data

Singularity containers required:

• khanlab/neuroglia-core:latest

Authors

• Your name here @yourgithubid

Usage

If you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this (original) repository and, if available, its DOI (see above).

Step 1: Obtain a copy of this workflow

1. Create a new github repository using this workflow as a template.
2. Clone the newly created repository to your local system, into the place where you want to perform the data analysis.

Step 2: Configure workflow

Configure the workflow according to your needs via editing the files in the config/ folder. Adjust config.yml to configure the workflow execution, and participants.tsv to specify your subjects.

Step 3: Install Snakemake

Install Snakemake using conda:

conda create -c bioconda -c conda-forge -n snakemake snakemake

For installation details, see the instructions in the Snakemake documentation.

Step 4: Execute workflow

Activate the conda environment:

conda activate snakemake

Test your configuration by performing a dry-run via

snakemake --use-singularity -n

Execute the workflow locally via

snakemake --use-singularity --cores $N

using $N cores or run it in a cluster environment via

snakemake --use-singularity --cluster qsub --jobs 100

or

snakemake --use-singularity --drmaa --jobs 100

If you are using Compute Canada, you can use the cc-slurm profile, which submits jobs and takes care of requesting the correct resources per job (including GPUs). Once it is set-up with cookiecutter, run:

snakemake --profile cc-slurm

Or, with neuroglia-helpers can get a 8-core, 32gb node and run locally there. First, get a node (default 8-core, 32gb, 3 hour limit):

regularInteractive 

Then, run:

snakemake --use-singularity --cores 8 --resources mem=32000 

See the Snakemake documentation for further details.

Step 5: Investigate results

After successful execution, you can create a self-contained interactive HTML report with all results via:

snakemake --report report.html

This report can, e.g., be forwarded to your collaborators. An example (using some trivial test data) can be seen here.

Step 6: Commit changes

Whenever you change something, don't forget to commit the changes back to your github copy of the repository:

git commit -a
git push

Step 7: Obtain updates from upstream

Whenever you want to synchronize your workflow copy with new developments from upstream, do the following.

1. Once, register the upstream repository in your local copy: git remote add -f upstream [email protected]:snakemake-workflows/{{cookiecutter.repo_name}}.git or git remote add -f upstream https://github.com/snakemake-workflows/{{cookiecutter.repo_name}}.git if you do not have setup ssh keys.
2. Update the upstream version: git fetch upstream.
3. Create a diff with the current version: git diff HEAD upstream/master workflow > upstream-changes.diff.
4. Investigate the changes: vim upstream-changes.diff.
5. Apply the modified diff via: git apply upstream-changes.diff.
6. Carefully check whether you need to update the config files: git diff HEAD upstream/master config. If so, do it manually, and only where necessary, since you would otherwise likely overwrite your settings and samples.

Step 8: Contribute back

In case you have also changed or added steps, please consider contributing them back to the original repository:

1. Fork the original repo to a personal or lab account.
2. Clone the fork to your local system, to a different place than where you ran your analysis.
3. Copy the modified files from your analysis to the clone of your fork, e.g., cp -r workflow path/to/fork. Make sure to not accidentally copy config file contents or sample sheets. Instead, manually update the example config files if necessary.
4. Commit and push your changes to your fork.
5. Create a pull request against the original repository.

Testing

TODO: create some test datasets