A hierarchical Bayesian model for flexible module discovery in three-way time-series data.

ABSTRACT:

Motivation

Detecting modules of co-ordinated activity is fundamental in the analysis of large biological studies. For two-dimensional data (e.g. genes × patients), this is often done via clustering or biclustering. More recently, studies monitoring patients over time have added another dimension. Analysis is much more challenging in this case, especially when time measurements are not synchronized. New methods that can analyze three-way data are thus needed.

Results

We present a new algorithm for finding coherent and flexible modules in three-way data. Our method can identify both core modules that appear in multiple patients and patient-specific augmentations of these core modules that contain additional genes. Our algorithm is based on a hierarchical Bayesian data model an

SUBMITTER: Amar D 

PROVIDER: S-EPMC4765869 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature