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scMethtools-- A scalable Python package for Single-cell DNA Methylation data analysis

Description

scMethtools is a toolkit designed for processing single-cell DNA methylation data. It can start from single-cell DNA methylation files generated by any upstream software, performing downstream analyses such as quality control, dimensionality reduction, clustering, and visualization. Developed in Python, scMethtools offers high extensibility.

This study develops the Python-based tool scMethtools for single-cell DNA methylation data analysis. This tool is a one-stop analytical framework designed to handle the complex characteristics of single-cell DNA methylation data, providing data processing, computation, and visualization functionalities. The overall architecture of scMethtools is inspired by the Scanpy package, which was designed for scRNA-seq data analysis (Wolf et al., 2018). The core data structure and operations rely on tools such as NumPy (Van Der Walt et al., 2011), Scipy (Virtanen et al., 2020), and Pandas. The computational methods are based on Scikit-learn (Pedregosa et al., 2011) and Statsmodels, while the visualization methods depend on Matplotlib (Hunter, 2007) and Seaborn (Waskom, 2021) etc.

scMethtools includes methods for:

  • Convert methylation data files to matrix objects
  • Quality control and feature selection
  • Clustering and differential analysis
  • Annotation of differentially methylated regions, DNA motif analysis, enhancer prediction, etc.

scMethtools

Authors:

  • Zong Wenting, developer

Cite:

If scMethtools has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing our paper [dd].