Statistical models for clustering dynamic gene expression profiles.

摘要

DNA microarray analysis has emerged as a leading technology to enhance our understanding of gene regulation and function in cellular mechanism controls on a genomic scale. This technology has advanced to unravel the genetic machinery of biological rhythms by collecting massive gene expression data during a time course. Currently available analysis of time-dependent gene expression data has been limited to the characterization of genes and arrays with similar expression patterns by using clustering approaches, with no consideration of the developmental mechanisms underlying gene expression. We present a general mixture model for cataloging time-dependent gene expression profiles in which the temporal pattern of gene expression is modeled by Fourier series approximations to determine periodic gene expression profiles and the time-dependent covariance matrix structured by autoregressive or antedependence models.;The model is also extended to a more general situation in which longitudinal gene expression profiles for each gene are measured at unequally spaced time intervals and different genes have different measurement patterns and is further extended to multiple experiments of gene expression. The advantages of this procedure lie in the biological relevance of results obtained and the construction of a general framework within which the interplay between gene expression and development can be tested.;We also implement the idea of wavelet dimension reduction into the mixture model for gene clustering, aimed to de-noise the data by transforming an inherently high-dimensional biological problem to its tractable low-dimensional representation. As a first attempt of its kind, we capitalize on the simplest Haar wavelet shrinkage technique to break an original signal down into spectrum by taking its averages and differences and, subsequently, to detect gene clusters that differ in the smooth coefficients extracting from noisy time series gene expression data. This wavelet-based model will have many implications for addressing biologically meaningful hypotheses at the interplay between gene actions/interactions and developmental pathways in various complex biological processes or networks.