Cited by 224Gene expression and cancer classificationBioinformatics and Genomic NetworksStatistical Methods in Clinical Trials

SLIM: a sliding linear model for estimating the proportion of true null hypotheses in datasets with dependence structures

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Wang, Hong-Qiang, Tuominen, Lindsey K., Tsai, Chung-Jui·2011·Bioinformatics

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.

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Authors
Wang, H.-Q., Tuominen, L. K., & Tsai, C.-J.
Affiliations
University of Georgia (US)
Corresponding author
Tsai, Chung-Jui
Venue
Bioinformatics
Published
2010-11-18
Volume
27
Issue
2
Pages
225-231
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
OpenAlex
W2167784579
Cited by
223
References
16
Citations by year
2026: 7 · 2025: 15 · 2024: 21 · 2023: 27 · 2022: 27 · 2021: 19 · 2020: 32 · 2019: 10 · 2018: 14 · 2017: 12 · 2016: 11 · 2015: 10 · 2014: 7 · 2013: 6 · 2012: 5
FWCI
1.43
Type
journal article
Language
en
Open access
Locations
  • Bioinformatics (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Science Foundation (0836433)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords
false discovery rate, multiple comparisons, dependence structure, Q-value, p-value distribution, microarray data, genomic data
MeSH terms
Algorithms, Computer Simulation, Linear Models, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Populus

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