generated from snakemake-workflows/snakemake-workflow-template
-
Notifications
You must be signed in to change notification settings - Fork 1
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
1 changed file
with
25 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1,7 +1,32 @@ | ||
# Workflow | ||
|
||
Under construction! | ||
|
||
## Overview | ||
|
||
SPIMprep is implemented in Python using the [Snakemake](https://snakemake.github.io/) workflow management system. It performs metadata extraction, flatfield correction (BaSiC), and stitching (BigStitcher), followed by the creation of a final validated BIDS dataset. | ||
|
||
## Inputs to SPIMprep | ||
|
||
|
||
|
||
|
||
## Installation | ||
|
||
The workflow is installed via pip, and the required container dependency is downloaded automatically by Snakemake. | ||
|
||
## Configuration | ||
|
||
Input datasets are configured using a TSV file, specifying subject identifiers and paths to folders/archives containing the raw TIF files, and a YAML file is used to customize workflow configuration. | ||
|
||
## Running Parallelization | ||
|
||
The workflow can be executed in parallel on any local, cluster or cloud resources, and each step is also internally parallelized (Dask), taking advantage of the parallelization afforded by the chunked file format. | ||
|
||
## Cloud support | ||
|
||
The workflow can optionally write directly to cloud storage, facilitating data sharing and interoperability with existing web-based viewers. | ||
|
||
## Outputs of SPIMprep | ||
|
||
|