Compound hierarchical correlated beta mixture with an application to cluster mouse transcription factor DNA binding data.

ABSTRACT: Modeling correlation structures is a challenge in bioinformatics, especially when dealing with high throughput genomic data. A compound hierarchical correlated beta mixture (CBM) with an exchangeable correlation structure is proposed to cluster genetic vectors into mixture components. The correlation coefficient, [Formula: see text], is homogenous within a mixture component and heterogeneous between mixture components. A random CBM with [Formula: see text] brings more flexibility in explaining correlation variations among genetic variables. Expectation-Maximization (EM) algorithm and Stochastic Expectation-Maximization (SEM) algorithm are used to estimate parameters of CBM. The number of mixture components can be determined using model selection criteria such as AIC, BIC and ICL-BIC. Exten

SUBMITTER: Dai H 

PROVIDER: S-EPMC4701176 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature