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Installing CompClust on OS X

Welcome to CompClust! We have not yet constructed an easy drag-and-drop CompClust .dmg installer for OS X, but we recognize the need and are planning to create one. In the meantime, if you are an adventurous software developer or system administrator you might consider installing CompClust from source for OS X.

These instructions and the associated resources are not fully complete, but at least offer a starting point for those that are motivated.


CompClust is written in Python and uses and requires a number of Python modules as well as graphics and statistics packages.

Many (if not all) of the dependencies can be met using the Fink software package management system. A (possibly imcomplete) list of the necessary packages can be obtained via Fink as follows:
  sudo apt-get install python24 ipython-py24 matplotlib-py24 \
		scientificpython-24 scipy-core-py24 numeric-py24 \
		numarray-py24 pmw rpy-py24 pygtk2-py24 pmw-py24 blt 

CompClust Source Installation on OS X

Once you've finished installing all of the dependencies using their fine instructions, you'll need to install CompClust. Thanks to the wonders of distutils, this should be relatively easy.

If it's not easy the DistUtils user manuals at http://www.python.org/doc/current/inst/inst.html might be useful.

To install the python code it boils down to running:
    unzip CompClust-1.2-src.zip      # extract the downloaded archive
    cd compclust                     # change to the subdirectory
    python setup.py install
Alternatively, rather than a complete "install", some success can also be achieved by simply using:
    python setup.py build --inplace
Of course that requires you to set your PYTHONPATH variable to the compclust source directory before using.

Additional Installation Resources

The complete list of prerequisite packages and detailed (though not OS-specific) instructions for building CompClust from source can be found in INSTALL.html, which is the HTML version of the INSTALL document found within the source code distribution.

Algorithm Installation

Because of licensing issues we can't distribute the source to the command line c code that we use.

We can provide binaries for our EM and KMeans clustering algorithms, but we have not yet packaged them for download.

We used the matlab dependent SOM toolbox from the Helsinki University of Technology for our SOM implementation. http://www.cis.hut.fi/projects/somtoolbox/

Also we used a modified version of XCluster which was based on the Gavin Sherlock's implementation. http://genetics.stanford.edu/~sherlock/cluster.html

For the binaries find a convenient location to store them.

For Xcluster you'll need to follow their build instructions.

The python code needs to know the location of these components for which we used environment variables to specify the paths to external programs. For instance the variable DIAGEM_COMMAND is used by the DiagEM wrapper to find the diagem executable.

  Wrapper    Required Environment variables

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