OCELOT:

Tool to gather and analyze clinical aspects of cancer datasets.
The initial implementation take the dataset just from GEO, but the architecture
of the project is thought to be easily extensible to other sources.

This project is based on the Django framework. A "Front End" (the admin
part will be missing) in PHP4 is also in development. 
PHP4 because then is easily usable on Apache based servers, 
even with an outdated configuration (yeah PHP5 came out 8 years ago...).


INSTALLATION:

  - Unix (OSX, Linux):
    Python 2.6 or newer is required.
    Installation with virtualenv:

     On OSX if virtualenv is not already installed:

      sudo easy_install pip
      sudo pip install virtualenv

     Once virtualenv is available:   
     
      mkdir ~/envs
      cd ~/envs/
      virtualenv --no-site-packages vDjango #(is possible to change the name vDjango in other)
      ./vDjango/bin/pip install Django
      ./vDjango/bin/pip install setproctitle
      ./vDjango/bin/pip install gunicorn

     Now it is possible to download the git trunk of ocelot:
     
      mkdir ~/djangoproj/
      cd ~/djangoproj/
      git clone git://github.com/francescofavero/ocelot.git
      
     Now it is possible to initialize the database:
     
      cd ocelot/
      ~/envs/vDjango/bin/python manage.py syncdb

     It case that the python installation is missing database support, it is possible
     to install it as well in the virtualenv:

      ~/envs/vDjango/bin/pip install sqlite3

     Once the database is initialized it is possible to run the Ocelot project:

      ~/envs/vDjango/bin/python manage.py run_gunicorn

     And point the browser at the http://127.0.0.1:8000 (the default address)
     
    R dependencies:
   
     For the analysis and plot generation, it is required the R statistical language, 
     available at www.r-project.org.

     the following R packages are also required:
     
      survplot: http://www.cbs.dtu.dk/~eklund/survplot/
      beeswarm: http://cran.r-project.org/web/packages/beeswarm/
     
     Once the R environment is installed is also required its python binding library, Rpy2:

      ~/envs/vDjango/bin/pip install rpy2

  
  - Windows:
     
     Probably this will also works on Windows machines once the Python language will be installed.
     Due to lack of resource we can't provide any tested Windows procedures.

Thanks!