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The Multiomics mOdule Correlation Analysis (MOCA) pipeline ver.1.1

Project description

Moca

MOCA (Multiomics mOdule Correlation Analysis) is a Python tool to comprehensively use coexpression module analysis and sparse canonical correlation analysis to identify modules, i.e., feature subsets that are highly correlated both within and between the omics levels.

MOCA is built on top of Python 2.7 and will be compatible with Python 3.7 in the near future.

MOCA is distributed under the GNU Lesser General Public License v3.0.

Installation

Using Docker

docker pull albertaki/jupyter-lab:0.35.4-moca

Using pip:

pip install moca-py

Using Pipenv:

pipenv install moca-py

Dependencies

Please note that you don't need to manually install the python dependencies.

For the pip and Pipenv users, please install the following R dependencies.

  • R (>= 3.4.4)
  • dynamicTreeCut
  • RGCCA
  • fastcluster

Changelog

Version 1.1 (beta)

  • Add sample alignment based ensemble for CCA
  • Add hard (subspace disjoint) deflation strategy for CCA
  • Add CCA loading tables ensemble for various component numbers
  • Add coexpression module differential analysis
  • Add ID mapping
  • Add plots for the pipelines
  • Add JupyterLab notebook
  • Add documentations
  • Add Docker images

Documentation

GitHub Pages: https://akialbert.github.io/Moca/

Citation

TO BE ADDED

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