title: Hierarchical Probabilistic Interaction Modeling for Multiple Gene Expression Replicates.
abstract: Microarray technology allows for the collection of multiple replicates of gene expression time course data for hundreds of genes at a handful of time points. Developing hypotheses about a gene transcriptional network, based on time course gene expression data is an important and very challenging problem. In many situations there are similarities which suggest a hierarchical structure between the replicates. This paper develops posterior probabilities for network features based on multiple hierarchical replications. Through Bayesian inference, in conjunction with the Metropolis-Hastings algorithm and model averaging, a hierarchical multiple replicate algorithm is applied to seven sets of simulated data and to a set of Arabidopsis thaliana gene expression data. The models of the simulated data suggest high posterior probabilities for pairs of genes which have at least moderate signal partial correlation. For the Arabidopsis model, many of the highest posterior probability edges agree with the literature.
authors.forename: Kristopher L
authors.initials: KL
authors.lastname: Patton
authors.forename: David J
authors.initials: DJ
authors.lastname: John
authors.forename: James L
authors.initials: JL
authors.lastname: Norris
authors.forename: Daniel R
authors.initials: DR
authors.lastname: Lewis
authors.forename: Gloria K
authors.initials: GK
authors.lastname: Muday
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics / Q000378:metabolism; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks / Q000235:genetics*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis