A Hidden Markov Model Approach to Testing Multiple Hypotheses on a Tree-Transformed Gene Ontology Graph

DOI10.1198/jasa.2010.tm10195zbMath1388.62327OpenAlexW2035923145MaRDI QIDQ5255685

Kun Liang, Dan Nettleton

Publication date: 17 June 2015

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1094&context=stat_las_preprints

zbMATH Keywords

differential expressionmultiple testingfalse discovery ratemicroarrayBayesian data analysisgene set enrichment analysisdirect acyclic graph

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Diagnostics, and linear inference and regression (62J20) Paired and multiple comparisons; multiple testing (62J15) Graphical methods in statistics (62A09)

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Adaptive Filtering Increases Power to Detect Differentially Expressed Genes ⋮ Variational Bayesian inference for association over phylogenetic trees for microorganisms ⋮ A multiple testing method for hypotheses structured in a directed acyclic graph ⋮ Covariate-adjusted multiple testing in genome-wide association studies via factorial hidden Markov models ⋮ Bayesian hidden Markov models for dependent large-scale multiple testing ⋮ Variable importance assessments and backward variable selection for multi-sample problems ⋮ A Hierarchical Semiparametric Model for Incorporating Intergene Information for Analysis of Genomic Data ⋮ False discovery rate estimation for large‐scale homogeneous discrete p‐values

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