Hierarchical Semiparametric Model for Incorporating Intergene Information for Analysis of Genomic Data

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 act...

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Veröffentlicht in:	Biometrics Jg. 68; H. 4; S. 1168 - 1177
Hauptverfasser:	Qu, Long, Nettleton, Dan, Dekkers, Jack C. M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Malden, USA Blackwell Publishing Inc 01.12.2012 Wiley-Blackwell Blackwell Publishing Ltd
Schlagworte:	A priori knowledge Algorithms Bioinformatics BIOMETRIC METHODOLOGY Biometrics biometry Chromosome Mapping - methods Computer Simulation Data analysis Data Interpretation, Statistical DNA, Intergenic - genetics Domain ontologies Exchange False discovery rates Gene expression Gene Expression Profiling - methods gene expression regulation Gene ontology Genes Genomics Hierarchical model Microarray microarray technology Modeling Models, Statistical Multidimensional scaling Nonparametric models Nonparametric regression Oligonucleotide Array Sequence Analysis - methods Parametric models Profiling Regression Regression Analysis Strategy
ISSN:	0006-341X, 1541-0420, 1541-0420
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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.
AbstractList	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. For analysis of genomic data, e.g., microarray data from gene expression profiling experiments, the twocomponent mixture model has been widely used in practice to detect differentially expressed genes. However, it naively 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. 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.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. 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. [PUBLICATION ABSTRACT] Summary 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 naively 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 naive two-component mixture model. Summary 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. Summary 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.
Author	Qu, Long Dekkers, Jack C. M. Nettleton, Dan
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/22994883$$D View this record in MEDLINE/PubMed
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Snippet	For analysis of genomic data, e.g., microarray data from gene expression profiling experiments, the two‐component mixture model has been widely used in... For analysis of genomic data, e.g., microarray data from gene expression profiling experiments, the twocomponent mixture model has been widely used in practice... Summary For analysis of genomic data, e.g., microarray data from gene expression profiling experiments, the two‐component mixture model has been widely used in... Summary For analysis of genomic data, e.g., microarray data from gene expression profiling experiments, the two‐component mixture model has been widely used in... For analysis of genomic data, e.g., microarray data from gene expression profiling experiments, the two-component mixture model has been widely used in... Summary For analysis of genomic data, e.g., microarray data from gene expression profiling experiments, the two-component mixture model has been widely used in...
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SubjectTerms	A priori knowledge Algorithms Bioinformatics BIOMETRIC METHODOLOGY Biometrics biometry Chromosome Mapping - methods Computer Simulation Data analysis Data Interpretation, Statistical DNA, Intergenic - genetics Domain ontologies Exchange False discovery rates Gene expression Gene Expression Profiling - methods gene expression regulation Gene ontology Genes Genomics Hierarchical model Microarray microarray technology Modeling Models, Statistical Multidimensional scaling Nonparametric models Nonparametric regression Oligonucleotide Array Sequence Analysis - methods Parametric models Profiling Regression Regression Analysis Strategy
Title	Hierarchical Semiparametric Model for Incorporating Intergene Information for Analysis of Genomic Data
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