Preprocessing methylation pipeline, written in python. Easy to use and highly parallelized.
Project description
PyMethylProcess
https://github.com/Christensen-Lab-Dartmouth/PyMethylProcess
Help documentation: https://christensen-lab-dartmouth.github.io/PyMethylProcess/
Alternatively, you can access the pdf: PyMethylProcess.pdf
What is it:
- Preprocess 450k and 850k methylation IDAT files in parallel using Minfi, ENmix, and meffil
- Convenient and scalable implementation
- Imputation and Feature Selection
- Preparation for machine learning pipelines
Why:
- Make DNAm accessible to python developers and more machine learning oriented researchers
- Streamlined analysis makes processing easy
Getting Started:
- Installation:
- pip install git+https://github.com/bodono/scs-python.git@bb45c69ce57b1fbb5ab23e02b30549a7e0b801e3 git+https://github.com/jlevy44/hypopt.git@27aefef62483174736bd6d5a1b3983dbaf4184dc
- pip install pymethylprocess && pymethyl-install_r_dependencies (Note: May need to prefix pip install with MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ for Mac OS install)
- docker pull joshualevy44/pymethylprocess
- Alternatively, run sh build_docker.sh to build the docker container, and then run sh run_docker.sh to run the docker container.
- Or see example scripts for usage.
- Example Usage Scripts (in github repo): Located in ./example_scripts/
- Help docs (in github repo): https://christensen-lab-dartmouth.github.io/PyMethylProcess/
PyMethyProcess is pending submission and review, and link to paper will be posted shortly.
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