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Full-Text Articles in Physical Sciences and Mathematics
Estimation And Testing Of Gene Expression Heterosis, Tieming Ji, Peng Liu, Dan Nettleton
Estimation And Testing Of Gene Expression Heterosis, Tieming Ji, Peng Liu, Dan Nettleton
Dan Nettleton
Heterosis, also known as the hybrid vigor, occurs when the mean phenotype of hybrid offspring is superior to that of its two inbred parents. The heterosis phenomenon is extensively utilized in agriculture though the molecular basis is still unknown. In an effort to understand phenotypic heterosis at the molecular level, researchers have begun to compare expression levels of thousands of genes between parental inbred lines and their hybrid offspring to search for evidence of gene expression heterosis. Standard statistical approaches for separately analyzing expression data for each gene can produce biased and highly variable estimates and unreliable tests of heterosis. …
Exploring The Information In P-Values For The Analysis And Planning Of Multiple-Test Experiments, David Ruppert, Dan Nettleton, J.T. Gene Hwang
Exploring The Information In P-Values For The Analysis And Planning Of Multiple-Test Experiments, David Ruppert, Dan Nettleton, J.T. Gene Hwang
Dan Nettleton
A new methodology is proposed for estimating the proportion of true null hypotheses in a large collection of tests. Each test concerns a single parameter δ whose value is specified by the null hypothesis. We combines a parametric model for the conditional CDF of the p-value given δ with a nonparametric spline model for the density g(δ) of δ under the alternative hypothesis. The proportion of true null hypotheses and the coefficients in the spline model are estimated by penalized least-squares subject to constraints that guarantee that the spline is a density. The estimator is computed efficiently using quadratic programming. …
A Hidden Markov Model Approach To Testing Multiple Hypotheses On A Gene Ontology Graph, Kun Liang, Dan Nettleton
A Hidden Markov Model Approach To Testing Multiple Hypotheses On A Gene Ontology Graph, Kun Liang, Dan Nettleton
Dan Nettleton
Gene category testing problems involve testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The logical relationships among the nodes in the graph imply that only some configurations of true and false null hypotheses are possible and that a test for a given node should depend on data from neighboring nodes. We developed a method based on a hidden Markov model that takes the whole graph into account and provides coherent decisions in this structured multiple hypothesis testing problem. The method is illustrated by testing Gene Ontology terms for evidence of differential expression.