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Preprocessing methylation pipeline, written in python. Easy to use and highly parallelized.

Project description

PyMethylProcess

pymethylprocess_overview

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

PyMethyProcess is pending submission and review, biorxiv: https://www.biorxiv.org/content/biorxiv/early/2019/04/13/604496.full.pdf.

Getting Started:

Benchmark Results: benchmark

Supplementary Figure Removed from Manuscript: Supplemental

Supplemental Figure 1: UMAP embeddings (colored) of: a) GSE87571 (age), b) GSE81961 (disease status), c) GSE69138 (subtype), d) GSE42861 (disease status), e) GSE112179 (brain disorder), f) GSE90496 (subclass), g) TCGA Pancancer (subtype)

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