SLIM: a sliding linear model for estimating the proportion of true null hypotheses in datasets with dependence structures.
Identifieur interne : 003000 ( Main/Curation ); précédent : 002F99; suivant : 003001SLIM: a sliding linear model for estimating the proportion of true null hypotheses in datasets with dependence structures.
Auteurs : Hong-Qiang Wang [États-Unis] ; Lindsey K. Tuominen ; Chung-Jui TsaiSource :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2011.
Descripteurs français
- KwdFr :
- MESH :
- génétique : Populus.
- métabolisme : Populus.
- Algorithmes, Analyse de profil d'expression de gènes, Modèles linéaires, Simulation numérique, Séquençage par oligonucléotides en batterie.
English descriptors
- KwdEn :
- MESH :
- genetics : Populus.
- metabolism : Populus.
- Algorithms, Computer Simulation, Gene Expression Profiling, Linear Models, Oligonucleotide Array Sequence Analysis.
Abstract
MOTIVATION
The pre-estimate of the proportion of null hypotheses (π(0)) plays a critical role in controlling false discovery rate (FDR) in multiple hypothesis testing. However, hidden complex dependence structures of many genomics datasets distort the distribution of p-values, rendering existing π(0) estimators less effective.
RESULTS
From the basic non-linear model of the q-value method, we developed a simple linear algorithm to probe local dependence blocks. We uncovered a non-static relationship between tests' p-values and their corresponding q-values that is influenced by data structure and π(0). Using an optimization framework, these findings were exploited to devise a Sliding Linear Model (SLIM) to more reliably estimate π(0) under dependence. When tested on a number of simulation datasets with varying data dependence structures and on microarray data, SLIM was found to be robust in estimating π(0) against dependence. The accuracy of its π(0) estimation suggests that SLIM can be used as a stand-alone tool for prediction of significant tests.
AVAILABILITY
The R code of the proposed method is available at http://aspendb.uga.edu/downloads for academic use.
DOI: 10.1093/bioinformatics/btq650
PubMed: 21098430
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pubmed:21098430Le document en format XML
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<term>Simulation numérique (MeSH)</term>
<term>Séquençage par oligonucléotides en batterie (MeSH)</term>
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<term>Oligonucleotide Array Sequence Analysis</term>
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<term>Analyse de profil d'expression de gènes</term>
<term>Modèles linéaires</term>
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<front><div type="abstract" xml:lang="en"><p><b>MOTIVATION</b>
</p>
<p>The pre-estimate of the proportion of null hypotheses (π(0)) plays a critical role in controlling false discovery rate (FDR) in multiple hypothesis testing. However, hidden complex dependence structures of many genomics datasets distort the distribution of p-values, rendering existing π(0) estimators less effective.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>RESULTS</b>
</p>
<p>From the basic non-linear model of the q-value method, we developed a simple linear algorithm to probe local dependence blocks. We uncovered a non-static relationship between tests' p-values and their corresponding q-values that is influenced by data structure and π(0). Using an optimization framework, these findings were exploited to devise a Sliding Linear Model (SLIM) to more reliably estimate π(0) under dependence. When tested on a number of simulation datasets with varying data dependence structures and on microarray data, SLIM was found to be robust in estimating π(0) against dependence. The accuracy of its π(0) estimation suggests that SLIM can be used as a stand-alone tool for prediction of significant tests.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>AVAILABILITY</b>
</p>
<p>The R code of the proposed method is available at http://aspendb.uga.edu/downloads for academic use.</p>
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