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Welcome to PyMethylProcess!

Levy,J.J. et al. (2019) PyMethylProcess - convenient high-throughput preprocessing workflow for DNA methylation data. Bioinformatics.

The goal of this Wiki is to provide more in depth examples other than those provided in ./example_scripts. Particularly, we're going to focus on a few items of interest:

1. Setting up and running an preprocessing analysis, explaining the command line tools and expected outputs along the way.
2. Basic Understanding of the primary datatypes.
3. Setting up and running traditional methylation analyses (cell-type deconvolution and age estimation).
4. Setting up and running machine learning analyses after the completion of the initial preprocessing. (UMAP and HDBSCAN, Random Forest, Imbalanced Learning, and Bayesian Hyperparameter Scans)
5. Finding important CpG contributions for these models using the Gini Index.

We are going to largely focus on this dataset: GSE87571