binarybana / samcnet

Bayesian model averaging of an objective function over a model class using advanced MCMC techniques.

SAMCNet

This package started as a toolkit and demonstration of Bayesian model averaging applied to a class of graphical models known as Bayesian networks. I then added functionality to perform optimal Bayesian Classification for a publication [Knight, Ivanov, Dougherty 2014]. In other words, it can handle classification of RNA-Seq data using a the published statistical model that shows superior performance when compared to nonlinear SVM, LDA, and others.

Both of these functionalities still work, although for cutting edge development, effort has moved over to the Julia ports for classification (OBC.jl), network inference (MCBN.jl), and a package split off to contain the MCMC methods at the API resolution I needed (SAMC.jl).

Installing

In order to use the classification component of the library, in a recent version of Ubuntu you'll need the following:

sudo apt-get install cython python-pandas python-numpy python-scipy git clone 
git://github.com/binarybana/samcnet.git
cd samcnet
./waf configure
./waf
export LD_LIBRARY_PATH=lib:build

Then test with

python -m tests.example

Usage

A video tutorial explaining how to operate the classifier on your RNA-Seq dataset has been posted at: http://www.youtube.com/watch?v=fPa5qy1tdhY

Network Inference (Deprecated)

If you'd like to use the network inference component, I highly recommend using the (non-abandoned) Julia port (MCBN.jl), but if you'd like to try this it'd look something like the following:

sudo apt-get install python-networkx libboost-dev libboost-program-options-dev 
libboost-test-dev libjudy-dev libgmp-dev python-networkx
cd samcnet
git submodule update --init
cd deps/libdai
cp Makefile.LINUX Makefile.conf
make -j
cd ../..
ln -s ../deps/libdai/lib/libdai.so lib/
for f in build/*.so; ln -s ../$f samcnet/; done

Building Blocks

This software would not be possible without the following components:

• Python for the main driving and glue code
• Cython for C and C++ integration and speed
• libdai for Bayesian network inference.
• Redis for the (optional) distributed job management
• waf for the build system
• rsyslog for remote logging