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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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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
A Direct Approach to False Discovery Rates
Storey, J. D. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00346 · Cited by 5,603
Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results
Lander, E. S., & Kruglyak, L. (1995) · Nature Genetics · doi:10.1038/ng1195-241 · Cited by 5,236
Size, power and false discovery rates
Efron, B. (2007) · The Annals of Statistics · doi:10.1214/009053606000001460 · Cited by 436
Pounds, S. B., & Morris, S. W. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg148 · Cited by 426
Correlation and Large-Scale Simultaneous Significance Testing
Efron, B. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001211 · Cited by 405
Metabolic Profiling as a Tool for Understanding Mechanisms of Toxicity
Clarke, C. J., & Haselden, J. N. (2008) · Toxicologic Pathology · doi:10.1177/0192623307310947 · Cited by 217
Robustness of multiple testing procedures against dependence
Clarke, S., & Hall, P. (2009) · The Annals of Statistics · doi:10.1214/07-aos557 · Cited by 95
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Xie, X.-P., Xie, Y., & Wang, H. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1794-6
Self-citationMetzger, D. C. H. (2018) · cIRcle (University of British Columbia) · doi:10.14288/1.0365265
Biswas, A., Chattopadhyay, G., & Chatterjee, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.13099
Biswas, A., & Chattopadhyay, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.03803
Liang, D., Aygün, N., Matoba, N., Ideraabdullah, F. Y., Love, M. I., & Stein, J. (2021) · bioRxiv · doi:10.1101/2021.10.04.463060
Venkataraman, Y. R., White, S. J., & Roberts, S. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.07.483338
(2016) · PROTEOMICS · doi:10.1002/pmic.201670084
An inverse Laplace transform oracle estimator for the normal means problem
Sijuwade, A., Chakraborty, S., & Dasgupta, N. (2023) · Metrika · doi:10.1007/s00184-023-00922-4
Snajder, R., Léger, A., Stegle, O., & Bonder, M. J. (2023) · Figshare · doi:10.6084/m9.figshare.22671533
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Muñoa-Hoyos, I., Araolaza, M., Lopez, I. C., Albizuri, M., & Subirán, N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.11.653026
Zhang, Q., Meng, X.-H., Qiu, C., Shen, H., Zhao, Q., Zhao, L.-J., Tian, Q., Sun, C.-Q., & HongWen, D. (2022) · Figshare · doi:10.6084/m9.figshare.19768042
Additional file 2 of DNA methylation entropy as a measure of stem cell replication and aging
Vaidya, H., Jeong, H. S., Keith, K., Maegawa, S., Calendo, G., Madžo, J., Jelı́nek, J., & Issa, J.-P. J. (2023) · Figshare · doi:10.6084/m9.figshare.22608889
Venkataraman, Y. R., White, S. J., & Roberts, S. B. (2022) · Figshare · doi:10.6084/m9.figshare.20438475
Zhang, Q., Meng, X.-H., Qiu, C., Shen, H., Zhao, Q., Zhao, L.-J., Tian, Q., Sun, C., & HongWen, D. (2022) · Figshare · doi:10.6084/m9.figshare.19768045
Additional file 1 of DNA methylation entropy as a measure of stem cell replication and aging
Vaidya, H., Jeong, H. S., Keith, K., Maegawa, S., Calendo, G., Madžo, J., Jelı́nek, J., & Issa, J.-P. J. (2023) · Figshare · doi:10.6084/m9.figshare.22608886
Additional file 3 of DNA methylation entropy as a measure of stem cell replication and aging
Vaidya, H., Jeong, H. S., Keith, K., Maegawa, S., Calendo, G., Madžo, J., Jelı́nek, J., & Issa, J.-P. J. (2023) · Figshare · doi:10.6084/m9.figshare.22608892
Snajder, R., Léger, A., Stegle, O., & Bonder, M. J. (2023) · Figshare · doi:10.6084/m9.figshare.22671530
Snajder, R., Léger, A., Stegle, O., & Bonder, M. J. (2023) · Figshare · doi:10.6084/m9.figshare.22671536
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Pythoud, N. (2020) · theses.fr (ABES) · doi:10.70675/659636cez02e9z4b19z9e6eze6ece92372f6
ACL injury reprograms quadriceps myonuclear epigenetic and transcriptional signatures in mice
Thomas, N. T., Brightwell, C. R., Carter, T., Keeble, A. R., Gonzalez‐Velez, S., Latham, C. M., Owen, A. M., Wen, Y., Murach, K. A., & Fry, C. S. (2026) · Molecular Medicine · doi:10.1186/s10020-026-01568-4
Yagound, B., Sarma, R. R., & Rollins, L. A. (2026) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2025.0030
Ma, Z., Xu, D., Qin, X., Li, S., Zhang, H., & Li, G. (2026) · Food Research International · doi:10.1016/j.foodres.2026.120841
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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