Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/judyboon/BASS

A Bayesian group factor model with structured sparsity
https://github.com/judyboon/BASS

Last synced: 28 days ago
JSON representation

A Bayesian group factor model with structured sparsity

Lists

README 

        
# BASS

This repository contains the implementation of _BASS_,  a group factor analysis model with Bayesian structured sparsity priors.

For details of the model, please see http://arxiv.org/abs/1411.2698. 

The draft is published on Journal of Machine Learning Research (JMLR).



## License

This software is distributed under the terms of the GNU General Public 

License as published by the Free Software Foundation, either version 3 

of the License, or (at your option) any later version.



## JMLR Copyright

This source code is supplied “as is” without warranty of any kind, 

and its author and the Journal of Machine Learning Research (JMLR) 

and JMLR’s publishers and distributors, disclaim any and all warranties, 

including but not limited to any implied warranties of merchantability 

and fitness for a particular purpose, and any warranties or non 

infringement. The user assumes all liability and responsibility for use 

of this source code, and neither the author nor JMLR, nor JMLR’s 

publishers and distributors, will be liable for damages of any kind 

resulting from its use. Without limiting the generality of the foregoing, 

neither the author, nor JMLR, nor JMLR’s publishers and distributors, 

warrant that the source code will be error-free, will operate without 

interruption, or will meet the needs of the user.



# Introduction

_BASS_ stands for Bayesian group factor Analysis with Structured Sparsity priors.

It extends traditional factor analysis model to _m_ coupled observation matrices (views).



BASS could be used to estimate both covariance specific to each observation matrix 

and shared variation across any subset of observations. 

Applications of BASS to construct condition specific gene co-expression networks and to

find shared topics among different classes of documents have been demonstrated in the paper.



# About the folders

* _simData_ contains simulation data sets used in the original paper.

* _code_ contains all source code of implementation.

* _output_ contains the output files/folders after running BASS.



## Code

### Compilation

BASS is implemented using C++. Use Makefile to compile the source code to an executable file. 

It requires installation of GNU Scientific Library (GSL). 

Make sure the library has been installed in your OS and 

the path has been correctly specified in _Makefile_.



### Running

Shell script _runBASS.sh_ provides an example of running BASS with simulation data set "Yn100sim5.sim".

It generates following commands

> ./main -y ../simData/Yn100sim5.sim -sep space -k 15 

  > -v 10 -p1 50 -p2 50 -p3 50 -p4 50 -p5 50 -p6 50 -p7 50 -p8 50 -p9 50 -p10 50

  > -n 100 -iter 20000 -step 50 -mcmc 0 -px 20 

  > -output ../output/MCMC0PX20ik15n100



Explanation of arguments:

* -y: joint data matrix file (p by n) with variables (features) are represented by row and subjects are in column

* -sep: how the data are separated. arguments: "space" "tab"

* -k: initial number of latent factors

* -v: number of observations (views)

* -p1 up to -pv: the dimension of each observation

* -n: number of subjects

* -iter: maximum iteration allowed

* -step: number of steps to store parameters

* -mcmc: number of MCMC steps

* -px: number of PX-EM steps

* -output: output directory



### Analyzing results

R script _analyze.R_ provides an example of analyzing outputs of BASS.



### Acknowledgement

The source code _RngStream.cpp_, _RngStream.h_ and *gig_par.cpp*, *gig_par.h* are downloaded from

http://jonaswallin.github.io/articles/2013/07/simulation-of-gig-distribution/ to draw samples from generalized inverse Gaussian distribution.