Snakemake workflow for SNSX32 hippocampal template creation.

This is a Snakemake workflow for propagating hippocampal segmentations from Jordan DeKraker's AutoTop (@jordandekraker): https://github.com/jordandekraker/Hippocampal_AutoTop It requires pre-processed T2SPACE data, and makes use of transforms from ANTS buildtemplate on the SNSX32 dataset.

Authors

Jonathan C. Lau (@jclauneuro)

Usage

Step 1: 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 2: Configure workflow

Configure the workflow according to your needs via editing the file config.yaml, and editing the participants.tsv

Step 3: Dry-run

Test your configuration by performing a dry-run via

snakemake -np

Step 4: Execution on graham (compute canada)

There are a few different ways to execute the workflow:

Execute the workflow locally using an interactive job
Execute the workflow using the cc-slurm profile

Interactive Job

Execute the workflow locally using an interactive job:

salloc --time=3:00:00 --gres=gpu:t4:1 --cpus-per-task=8 --ntasks=1 --mem=32000 --account=YOUR_CC_ACCT srun snakemake --use-singularity --cores 8 --resources gpus=1 mem_mb=32000

Use the cc-slurm profile

The cc-slurm profile sets up default options for running on compute canada systems. More info in the README here: https://github.com/khanlab/cc-slurm

If you haven't used it before, deploy the cc-slurm profile using:

cookiecutter gh:khanlab/cc-slurm -o ~/.config/snakemake -f

Note: you must have cookiecutter installed (e.g. pip install cookiecutter)

Then to execute the workflow for all subjects, submitting a job for each rule group, use:

snakemake --profile cc-slurm

In summary, a useful series of commands leading up to submission on a cluster:

snakemake -np
snakemake --dag | dot -Tpdf > dag.pdf
snakemake --profile cc-slurm --use-singularity

Export to Dropbox

To export files to dropbox, use: snakemake -s export_dropbox.smk

See the Snakemake documentation for further details.

Step 4: 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.

Advanced

The following recipe provides established best practices for running and extending this workflow in a reproducible way.

Fork the repo to a personal or lab account.
Clone the fork to the desired working directory for the concrete project/run on your machine.
Create a new branch (the project-branch) within the clone and switch to it. The branch will contain any project-specific modifications (e.g. to configuration, but also to code).
Modify the config, and any necessary sheets (and probably the workflow) as needed.
Commit any changes and push the project-branch to your fork on github.
Run the analysis.
Optional: Merge back any valuable and generalizable changes to the upstream repo via a pull request. This would be greatly appreciated.
Optional: Push results (plots/tables) to the remote branch on your fork.
Optional: Create a self-contained workflow archive for publication along with the paper (snakemake --archive).
Optional: Delete the local clone/workdir to free space.

Testing

No test cases yet

jclauneuro/snakemakehippo