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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2511.21562 |
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| _version_ | 1866909926769557504 |
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| author | Lee, Sang Kyu Zhang, Tongwu Hong, Hyokyoung G. Weng, Haolei |
| author_facet | Lee, Sang Kyu Zhang, Tongwu Hong, Hyokyoung G. Weng, Haolei |
| contents | Quantifying how genomic features influence different parts of an outcome distribution requires statistical tools that go beyond mean regression, especially in ultrahigh-dimensional settings. Motivated by the study of LINE-1 activity in cancer, we propose StaRQR-K, a stabilized regional quantile regression framework with model-X knockoffs for false discovery rate control. StaRQR-K identifies CpG sites whose methylation levels are associated with specific quantile regions of an outcome, allowing detection of heterogeneous and tail-sensitive effects. The method combines an efficient regional quantile sure independence screening procedure with a winsorizing-based model-X knockoff filter, providing false discovery rate (FDR) control for regional quantile regression. Simulation studies show that StaRQR-K achieves valid FDR control and substantially higher power than existing approaches. In an application to The Cancer Genome Atlas head and neck cancer cohort, StaRQR-K reveals quantile-region-specific associations between CpG methylation and LINE-1 activity that improve out-of-sample prediction and highlight genomic regions with known functional relevance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21562 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | StaRQR-K: False Discovery Rate Controlled Regional Quantile Regression Lee, Sang Kyu Zhang, Tongwu Hong, Hyokyoung G. Weng, Haolei Methodology Quantifying how genomic features influence different parts of an outcome distribution requires statistical tools that go beyond mean regression, especially in ultrahigh-dimensional settings. Motivated by the study of LINE-1 activity in cancer, we propose StaRQR-K, a stabilized regional quantile regression framework with model-X knockoffs for false discovery rate control. StaRQR-K identifies CpG sites whose methylation levels are associated with specific quantile regions of an outcome, allowing detection of heterogeneous and tail-sensitive effects. The method combines an efficient regional quantile sure independence screening procedure with a winsorizing-based model-X knockoff filter, providing false discovery rate (FDR) control for regional quantile regression. Simulation studies show that StaRQR-K achieves valid FDR control and substantially higher power than existing approaches. In an application to The Cancer Genome Atlas head and neck cancer cohort, StaRQR-K reveals quantile-region-specific associations between CpG methylation and LINE-1 activity that improve out-of-sample prediction and highlight genomic regions with known functional relevance. |
| title | StaRQR-K: False Discovery Rate Controlled Regional Quantile Regression |
| topic | Methodology |
| url | https://arxiv.org/abs/2511.21562 |