title: A hierarchical semiparametric model for incorporating intergene information for analysis of genomic data.
abstract: For analysis of genomic data, e.g., microarray data from gene expression profiling experiments, the two-component mixture model has been widely used in practice to detect differentially expressed genes. However, it naïvely imposes strong exchangeability assumptions across genes and does not make active use of a priori information about intergene relationships that is currently available, e.g., gene annotations through the Gene Ontology (GO) project. We propose a general strategy that first generates a set of covariates that summarizes the intergene information and then extends the two-component mixture model into a hierarchical semiparametric model utilizing the generated covariates through latent nonparametric regression. Simulations and analysis of real microarray data show that our method can outperform the naïve two-component mixture model.
authors.affiliation: BioStat Solutions, Inc., MD 21771, USA.
authors.forename: Long
authors.initials: L
authors.lastname: Qu
authors.forename: Dan
authors.initials: D
authors.lastname: Nettleton
authors.forename: Jack C M
authors.initials: JC
authors.lastname: Dekkers
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D021901:DNA, Intergenic / Q000235:genetics*; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012044:Regression Analysis
chemical_list: D021901:DNA, Intergenic