The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
SLIM: a sliding linear model for estimating the proportion of true null hypotheses in datasets with dependence structures
Open accessAbstract
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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“P-values were adjusted for multiple testing (i.e., q-value) via the SLIM method80, and overdispersion correction was applied.”
From Integrated analysis of behavioral, epigenetic, and gut microbiome analyses in AppNL-G-F, AppNL-F, and wild type mice (2021) · cites this paper as [80]
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Self-citationEstimating the Proportion of True Null Hypotheses for Multiple Comparisons
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Metadata
- Authors
- Wang, H.-Q., Tuominen, L. K., & Tsai, C.-J.
- Affiliations
- University of Georgia (US)
- Corresponding author
- 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
- 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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