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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Vydáno v:Biometrics Ročník 68; číslo 4; s. 1168 - 1177
Hlavní autoři: Qu, Long, Nettleton, Dan, Dekkers, Jack C. M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Malden, USA Blackwell Publishing Inc 01.12.2012
Wiley-Blackwell
Blackwell Publishing Ltd
Témata:
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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Shrnutí: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.
Bibliografie:http://dx.doi.org/10.1111/j.1541-0420.2012.01778.x
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ArticleID:BIOM1778
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0006-341X
1541-0420
1541-0420
DOI:10.1111/j.1541-0420.2012.01778.x