A toolkit that allows users to apply NiChart processing methods and pre-trained models on their data. NiChart^Workflows includes tools for image processing, data harmonization and machine learning, using pre-trained models and reference distributions provided by NiChart^Engine. NiChart^Workflow utilizes Snakemake workflows to define multi-step image processing and data analytics pipelines.

Install dependencies for the NiChart^Workflows in a conda environment using the following commands. NiChart^Workflows uses snakemake to automate things, which is (most easily) installed with mamba.

conda install -n base -c conda-forge mamba # If needed
mamba init # If first time installing, then potentially source ~/.bashrc
mamba create -c conda-forge -c bioconda -n NiChart_Workflows python=3.8 
mamba activate NiChart_Workflows
pip install spare-scores NiChartHarmonize
mamba install -c conda-forge -c bioconda snakemake

Users can apply NiChart workflows to their data with a few simple steps:

1. Copy Data: Transfer your data to the designated location within the project directory.

2. Edit Configuration File: Make any necessary adjustments to the configuration file as specified by the package documentation.

3. Run Workflow: Execute the Snakemake workflow to initiate the analysis.

! For successful workflow execution, user data should meet specific requirements:

• Input scans should be copied to a folder named:

  "data/{study name}/Images"

  where {study name} is a variable for the dataset or study.

  Users can create multiple "data/{study name}" folders. Each dataset or study is processed independently, with results saved in sub-folders of the study folder.

• A list named:

  "data/{study name}/Lists/{study name}_Demog.csv"

  with demographic information for each subject. The list should include the columns

  • MRID: ID of each scan
  • Age: Age of the subject
  • Sex: Sex of the subject (M/F)

We provided data for a toy dataset "data/Study1" as an example. Please use it as a reference for your input data. You can apply the structural MRI workflow on this dataset as follows:

cd workflows/w_sMRI
snakemake -np           ## Dry run
snakemake --cores 1     ## Run the pipeline on the Study1

If the workflow works successfully, results will be created inside the study folder, e.g.:

"data/Study1/output",

We welcome contributions from the community! If you have bug fixes, improvements, or new features, please consider creating a pull request. Before submitting a pull request, please:

• Ensure your code adheres to the existing code style and formatting.
• Include clear documentation for your changes.
• Write unit tests for any new functionality.

This project is licensed under the [License Name] license. Please refer to the LICENSE file for the full license text. (Replace [License Name] with the actual license used by your project, such as MIT, Apache, or BSD)

For any inquiries, please contact guray.erus@pennmedicine.upenn.edu. (Last Updated: 5/24/2024)