Functional clustering of periodic transcriptional profiles through ARMA(p,q).

ABSTRACT:

Background

Gene clustering of periodic transcriptional profiles provides an opportunity to shed light on a variety of biological processes, but this technique relies critically upon the robust modeling of longitudinal covariance structure over time.

Methodology

We propose a statistical method for functional clustering of periodic gene expression by modeling the covariance matrix of serial measurements through a general autoregressive moving-average process of order (p,q), the so-called ARMA(p,q). We derive a sophisticated EM algorithm to estimate the proportions of each gene cluster, the Fourier series parameters that define gene-specific differences in periodic expression trajectories, and the ARMA parameters that model the covariance structure within a mixture model framew

SUBMITTER: Li N 

PROVIDER: S-EPMC2855703 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature