Skip to main content

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

Project description


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.


Using Docker

docker pull albertaki/jupyter-lab:0.35.4-moca

Using pip:

pip install moca-py

Using Pipenv:

pipenv install moca-py


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
  • fastcluster


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


GitHub Pages:



Project details

Release history Release notifications

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for moca-py, version 1.1.26
Nom du fichier, taille File type Version de Python Date de publication Hashes
Nom du fichier, taille moca_py-1.1.26-py2-none-any.whl (59.1 kB) File type Wheel Version de Python py2 Date de publication Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page