Coconut: covariate-assisted composite null hypothesis testing with applications to replicability analysis of high-throughput experimental data
Abstract Background Multiple testing of composite null hypotheses is critical for identifying simultaneous signals across studies. While it is common to incorporate external information in simple null hypotheses, exploiting such auxiliary covariates to provide prior structural relationships among co...
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| Format: | Article |
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BMC
2025-07-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06163-8 |
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| author | Yan Li Yanmei Li Han Ma Zitong Yue Xin Zhang |
| author_facet | Yan Li Yanmei Li Han Ma Zitong Yue Xin Zhang |
| author_sort | Yan Li |
| collection | DOAJ |
| description | Abstract Background Multiple testing of composite null hypotheses is critical for identifying simultaneous signals across studies. While it is common to incorporate external information in simple null hypotheses, exploiting such auxiliary covariates to provide prior structural relationships among composite null hypotheses and boost the statistical power remains challenging. Results We propose a robust and powerful covariate-assisted composite null hypothesis testing (CoCoNuT) procedure based on a Bayesian framework to identify replicable signals in two studies while asymptotically controlling the false discovery rate. CoCoNuT innovatively adopts a three-dimensional mixture model to consider two primary studies and an integrative auxiliary covariate jointly. While accounting for heterogeneity across studies, the local false discovery rate optimally captures cross-study and cross-feature information, providing improved rankings of feature importance. Conclusions Theoretical and empirical evaluations confirm the validity and efficiency of CoCoNuT. Extensive simulations demonstrate that CoCoNuT outperforms conventional methods that do not exploit auxiliary covariates while controlling the FDR. We apply CoCoNuT to schizophrenia genome-wide association studies, illustrating its higher power in identifying replicable genetic variants with the assistance of relevant auxiliary studies. |
| format | Article |
| id | doaj-art-a95ca933628e44489cd99cf1766e9676 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-a95ca933628e44489cd99cf1766e96762025-08-20T04:01:36ZengBMCBMC Bioinformatics1471-21052025-07-0126111810.1186/s12859-025-06163-8Coconut: covariate-assisted composite null hypothesis testing with applications to replicability analysis of high-throughput experimental dataYan Li0Yanmei Li1Han Ma2Zitong Yue3Xin Zhang4School of Computer Science and Technology, Changchun University of Science and TechnologySchool of Computer Science and Technology, Changchun University of Science and TechnologySchool of Computer Science and Technology, Changchun University of Science and TechnologySchool of Business, Hong Kong University of Science and TechnologySchool of Computer Science and Technology, Changchun University of Science and TechnologyAbstract Background Multiple testing of composite null hypotheses is critical for identifying simultaneous signals across studies. While it is common to incorporate external information in simple null hypotheses, exploiting such auxiliary covariates to provide prior structural relationships among composite null hypotheses and boost the statistical power remains challenging. Results We propose a robust and powerful covariate-assisted composite null hypothesis testing (CoCoNuT) procedure based on a Bayesian framework to identify replicable signals in two studies while asymptotically controlling the false discovery rate. CoCoNuT innovatively adopts a three-dimensional mixture model to consider two primary studies and an integrative auxiliary covariate jointly. While accounting for heterogeneity across studies, the local false discovery rate optimally captures cross-study and cross-feature information, providing improved rankings of feature importance. Conclusions Theoretical and empirical evaluations confirm the validity and efficiency of CoCoNuT. Extensive simulations demonstrate that CoCoNuT outperforms conventional methods that do not exploit auxiliary covariates while controlling the FDR. We apply CoCoNuT to schizophrenia genome-wide association studies, illustrating its higher power in identifying replicable genetic variants with the assistance of relevant auxiliary studies.https://doi.org/10.1186/s12859-025-06163-8Composite null hypothesisAuxiliary informationReplicabilityHigh-throughput experimentsCauchy combination |
| spellingShingle | Yan Li Yanmei Li Han Ma Zitong Yue Xin Zhang Coconut: covariate-assisted composite null hypothesis testing with applications to replicability analysis of high-throughput experimental data BMC Bioinformatics Composite null hypothesis Auxiliary information Replicability High-throughput experiments Cauchy combination |
| title | Coconut: covariate-assisted composite null hypothesis testing with applications to replicability analysis of high-throughput experimental data |
| title_full | Coconut: covariate-assisted composite null hypothesis testing with applications to replicability analysis of high-throughput experimental data |
| title_fullStr | Coconut: covariate-assisted composite null hypothesis testing with applications to replicability analysis of high-throughput experimental data |
| title_full_unstemmed | Coconut: covariate-assisted composite null hypothesis testing with applications to replicability analysis of high-throughput experimental data |
| title_short | Coconut: covariate-assisted composite null hypothesis testing with applications to replicability analysis of high-throughput experimental data |
| title_sort | coconut covariate assisted composite null hypothesis testing with applications to replicability analysis of high throughput experimental data |
| topic | Composite null hypothesis Auxiliary information Replicability High-throughput experiments Cauchy combination |
| url | https://doi.org/10.1186/s12859-025-06163-8 |
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