ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2018
False discovery rate control for high dimensional networks of quantile associations conditioning on covariates.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- SifiNet: a robust and accurate method to identify feature gene sets and annotate cells.Nucleic acids research · 2024Article
- A double-robust test for high-dimensional gene coexpression networks conditioning on clinical information.Biometrics · 2023Article
- TEAM: A MULTIPLE TESTING ALGORITHM ON THE AGGREGATION TREE FOR FLOW CYTOMETRY ANALYSIS.The annals of applied statistics · 2023Article
- DART: Distance Assisted Recursive Testing.Journal of machine learning research : JMLR · 2023Article
- Taylor quasi-likelihood for limited generalized linear models.Journal of applied statistics · 2021Article
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Authors and funding
2 authors.
Funding
Abstract
Motivated by gene coexpression pattern analysis, we propose a novel sample quantile contingency (SQUAC) statistic to infer quantile associations conditioning on covariates. It features enhanced flexibility in handling variables with both arbitrary distributions and complex association patterns conditioning on covariates. We first derive its asymptotic null distribution, and then develop a multiple-testing procedure based on the SQUAC statistic to test simultaneously the independence between one pair of variables conditioning on covariates for all
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