Biomaterial Database

On the geometric modeling approach to empirical null distribution estimation for empirical Bayes modeling of multiple hypothesis testing. 2013

Baolin Wu

Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA. baolin@umn.edu

We study the geometric modeling approach to estimating the null distribution for the empirical Bayes modeling of multiple hypothesis testing. The commonly used method is a nonparametric approach based on the Poisson regression, which however could be unduly affected by the dependence among test statistics and perform very poorly under strong dependence. In this paper, we explore a finite mixture model based geometric modeling approach to empirical null distribution estimation and multiple hypothesis testing. Through simulations and applications to two public microarray data, we will illustrate its competitive performance.

UI	MeSH Term	Description	Entries
D007938	Leukemia	A progressive, malignant disease of the blood-forming organs, characterized by distorted proliferation and development of leukocytes and their precursors in the blood and bone marrow. Leukemias were originally termed acute or chronic based on life expectancy but now are classified according to cellular maturity. Acute leukemias consist of predominately immature cells; chronic leukemias are composed of more mature cells. (From The Merck Manual, 2006)	Leucocythaemia,Leucocythemia,Leucocythaemias,Leucocythemias,Leukemias
D008297	Male		Males
D011471	Prostatic Neoplasms	Tumors or cancer of the PROSTATE.	Cancer of Prostate,Prostate Cancer,Cancer of the Prostate,Neoplasms, Prostate,Neoplasms, Prostatic,Prostate Neoplasms,Prostatic Cancer,Cancer, Prostate,Cancer, Prostatic,Cancers, Prostate,Cancers, Prostatic,Neoplasm, Prostate,Neoplasm, Prostatic,Prostate Cancers,Prostate Neoplasm,Prostatic Cancers,Prostatic Neoplasm
D003198	Computer Simulation	Computer-based representation of physical systems and phenomena such as chemical processes.	Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D001499	Bayes Theorem	A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.	Bayesian Analysis,Bayesian Estimation,Bayesian Forecast,Bayesian Method,Bayesian Prediction,Analysis, Bayesian,Bayesian Approach,Approach, Bayesian,Approachs, Bayesian,Bayesian Approachs,Estimation, Bayesian,Forecast, Bayesian,Method, Bayesian,Prediction, Bayesian,Theorem, Bayes
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D020411	Oligonucleotide Array Sequence Analysis	Hybridization of a nucleic acid sample to a very large set of OLIGONUCLEOTIDE PROBES, which have been attached individually in columns and rows to a solid support, to determine a BASE SEQUENCE, or to detect variations in a gene sequence, GENE EXPRESSION, or for GENE MAPPING.	DNA Microarrays,Gene Expression Microarray Analysis,Oligonucleotide Arrays,cDNA Microarrays,DNA Arrays,DNA Chips,DNA Microchips,Gene Chips,Oligodeoxyribonucleotide Array Sequence Analysis,Oligonucleotide Microarrays,Sequence Analysis, Oligonucleotide Array,cDNA Arrays,Array, DNA,Array, Oligonucleotide,Array, cDNA,Arrays, DNA,Arrays, Oligonucleotide,Arrays, cDNA,Chip, DNA,Chip, Gene,Chips, DNA,Chips, Gene,DNA Array,DNA Chip,DNA Microarray,DNA Microchip,Gene Chip,Microarray, DNA,Microarray, Oligonucleotide,Microarray, cDNA,Microarrays, DNA,Microarrays, Oligonucleotide,Microarrays, cDNA,Microchip, DNA,Microchips, DNA,Oligonucleotide Array,Oligonucleotide Microarray,cDNA Array,cDNA Microarray