Neuroscience Data Analysis Pipelines with Python, Git, and Snakemake
Build robust, reproducible analysis pipelines with Snakemake, Conda, and Git for scalable computational neuroscience projects.
Authors
As neuroscience evolves with increasingly large data sets, complex analyses, and bigger teams, building robust, reproducible, and scalable analysis workflows becomes essential in order to develop large computational science projects and keep them sustainable over time.
In this course, you will gain hands-on experience in crafting modular code scripts and command-line tools for efficient data processing, and how to cleanly combine them into a complete analysis pipeline, using Python’s snakemake package. We will explore tools such as Conda package manager to ensure consistent computational environments, vital for sharing code and ensuring computational reproducibility, and Git and GitHub for collaborating and sharing code across teams.
By the end of this course, you’ll be able to develop and manage data analysis pipelines, ensuring that your projects are not only advanced in their execution but also sustainable in the long term - your future self will thank you for it!
Credits
Installation
To run the course materials on your own machine, it is recommended that you:
- Install VSCode as your editor
- Install pixi or alternatively conda to create virtual Python environments (see the lessons on environment and package management)
- Create a dedicated folder for this course and install the virtual environment:
Download the pixi.toml file and install the environment:
pixi install --manifest-path pixi.toml
pixi shellDownload the environment.yml file and install the environment:
conda env create -f environment.yml
conda activate snakemakeCourse Contents
Git, GitHub, and Python Scripts
Python Scripts
In data analysis, Python scripts help automate standard preprocessing and standardization steps so that new datasets can be processed efficiently without rewriting code each time.
Creating CLIs with Python
Command-Line Interfaces (CLIs) make scripts more powerful and user-friendly, allowing for easier use.
Python Modules
Python Functions
Introduction to creating, organizing, and documenting Python functions with exercises on basic functions, refactoring, optional arguments, and docstrings.
Python Modules
Introduction to creating basic modules.