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

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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

DOI 10.1093/bioinformatics/btq650

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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
ORCID: Tuominen, Lindsey K.
Tsai, Chung-Jui
Venue: Bioinformatics
Publisher: Oxford University Press (OUP)
Published: 2010-11-18
Volume: 27
Issue: 2
Pages: 225-231
ISSN: 1367-4803 (ISSN-L)
1367-4811
DOI: 10.1093/bioinformatics/btq650
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: bronze
doi.org
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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