CoGAPS-package


CoGAPS implements a Bayesian MCMC matrix factorization algorithm,
    GAPS, and links it to gene set statistic methods to infer
     biological process activity.  It can be used to perform
      sparse matrix factorization on any data, and when this 
      data represents biomolecules, to do gene set analysis.<p></p><p><table><tr><td>
Package: </td>
</tr><tr><td> CoGAPS</td>
</tr><tr><td>
Type: </td>
</tr><tr><td> Package</td>
</tr><tr><td>
Version: </td>
</tr><tr><td> 1.0</td>
</tr><tr><td>
Date: </td>
</tr><tr><td> 2014-07-23</td>
</tr><tr><td>
License: </td>
</tr><tr><td> LGPL</td>
</tr></table> </p>

Coordinated Gene Activity in Pattern Sets (CoGAPS)
implements a Bayesian MCMC matrix factorization algorithm,
GAPS, and links it to gene set statistic methods to infer biological
process activity.  It can be used to perform sparse matrix factorization on
any data, and when this data represents biomolecules, to do gene set
analysis.

Elana J Fertig

CoGAPS

Coordinated Gene Activity in Pattern Sets

CoGAPS-package function

CoGAPS implements a Bayesian MCMC matrix factorization algorithm,
    GAPS, and links it to gene set statistic methods to infer
     biological process activity.  It can be used to perform
      sparse matrix factorization on any data, and when this 
      data represents biomolecules, to do gene set analysis.

<tabular>ll</tabular>{
Package: 	CoGAPSType: 	PackageVersion: 	1.0Date: 	2014-07-23License: 	LGPL}

CoGAPS-package: CoGAPS: Coordinated Gene Analysis Pattern Sets

Description

Arguments

References