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SLIM: a sliding linear model for estimating the proportion of true null hypotheses in datasets with dependence
Hong-Qiang Wang1, Lindsey K Tuominen, Chung-Jui Tsai
1Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA.
Bioinformatics (Oxford, England)
|November 25, 2010
Summary
A new method, the Sliding Linear Model (SLIM), reliably estimates the proportion of null hypotheses (π(0)) even with complex genomic data structures. This robust estimation improves false discovery rate control in multiple hypothesis testing.
Area of Science:
- Genomics
- Statistical Genetics
- Bioinformatics
Background:
- Accurate estimation of the proportion of null hypotheses (π(0)) is crucial for controlling the false discovery rate (FDR) in multiple hypothesis testing.
- Complex dependence structures in genomics data can distort p-value distributions, reducing the effectiveness of existing π(0) estimation methods.
Purpose of the Study:
- To develop a novel and robust method for estimating π(0) that accounts for complex data dependence structures.
- To improve the reliability of FDR control in genomics studies by enhancing π(0) estimation.
Main Methods:
- Developed a simple linear algorithm to analyze local dependence blocks based on the q-value method's non-linear model.
- Devised the Sliding Linear Model (SLIM) using an optimization framework to exploit the relationship between p-values and q-values under dependence.
- Tested SLIM on diverse simulation datasets with varying dependence structures and on real microarray data.
Main Results:
- Uncovered a non-static relationship between p-values and q-values influenced by data structure and π(0).
- SLIM demonstrated robustness in estimating π(0) across different data dependence structures.
- The accuracy of SLIM's π(0) estimation suggests its utility as a standalone tool for predicting significant tests.
Conclusions:
- The Sliding Linear Model (SLIM) provides a reliable method for estimating π(0) in the presence of complex data dependencies.
- SLIM offers improved accuracy for π(0) estimation, enhancing the control of FDR in genomic analyses.
- The developed R code is available for academic use, facilitating the application of SLIM in research.
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