dreiss-isb / cmonkey-python

Python port of cMonkey, a machine-learning based method for clustering

cMonkey Python - Python port of the cMonkey biclustering algorithm

Description

This is the Python implementation of the cMonkey algorithm based on the original R implementation by David Reiss, Institute for Systems Biology

System requirements

• Developed and tested with Python 2.7.2
• scipy >= 0.9.0
• numpy >= 1.6.0
• MySQLdb >= 1.2.3
• BeautifulSoup >= 3.2.0
• R >= 2.14.1
• rpy2 >= 2.2.1
• MEME 4.3.0 or >= 4.8.1
• csh (for running MEME) for the human setup, Weeder 1.4.2 is needed

for running the monitoring application (optional):

• CherryPy 3
• Jinja2
• python-routes

Running the Unit Tests

./run_tests.sh

Running cMonkey

In general, you should be able to run cmonkey-python on microbial gene expressions with

./cmonkey.py --organism <organism-code> --ratios <tab separated file of gene expressions>

The file can be either in your file system or a web URL.

After the program was started, a log file will be written in cmonkey.log. You can see all available options with

./cmonkey.py --help

Test Run with Halobacterium Salinarum

There is a startup script for cMonkey to run the current integrated system

./cmonkey.py --organism hal --ratios example_data/hal/halo_ratios5.tsv

Start the python based monitoring application

python cmviewer/main.py