Journal ArticleDOI
Large-scale multiple testing under dependence
Wenguang Sun,T. Tony Cai +1 more
TLDR
In this paper, the problem of multiple testing under dependence in a compound decision theoretic framework is considered, where the observed data are assumed to be generated from an underlying two-state hidden Markov model.Abstract:
Summary. The paper considers the problem of multiple testing under dependence in a compound decision theoretic framework. The observed data are assumed to be generated from an underlying two-state hidden Markov model. We propose oracle and asymptotically optimal datadriven procedures that aim to minimize the false non-discovery rate FNR subject to a constraint on the false discovery rate FDR. It is shown that the performance of a multiple-testing procedure can be substantially improved by adaptively exploiting the dependence structure among hypotheses, and hence conventional FDR procedures that ignore this structural information are inefficient. Both theoretical properties and numerical performances of the procedures proposed are investigated. It is shown that the procedures proposed control FDR at the desired level, enjoy certain optimality properties and are especially powerful in identifying clustered non-null cases. The new procedure is applied to an influenza-like illness surveillance study for detecting the timing of epidemic periods.read more
Citations
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Journal ArticleDOI
A Markov random field model for network-based analysis of genomic data
Zhi Wei,Hongzhe Li +1 more
TL;DR: A Markov random field (MRF)-based model efficiently utilizes the known pathway structures in identifying the DE genes and the subnetworks that might be related to phenotype.
Journal ArticleDOI
Estimating False Discovery Proportion Under Arbitrary Covariance Dependence
Jianqing Fan,Xu Han,Weijie Gu +2 more
TL;DR: In this article, a principal factor approximation (PFA) based method was proposed to solve the problem of false discovery control in large-scale multiple hypothesis testing, where a common threshold is used and a consistent estimate of realized FDP is provided.
Journal ArticleDOI
Travelling the world of gene–gene interactions
TL;DR: A perspective view on a selection of currently active analysis strategies and concerns in the context of epistasis detection, and to provide an eye to the future of gene-gene interaction analysis are provided.
Journal ArticleDOI
Correlated z-values and the accuracy of large-scale statistical estimates.
TL;DR: This paper concerns the accuracy of summary statistics for the collection of normal variates, such as their empirical cdf or a false discovery rate statistic, and shows that good accuracy approximations can be based on the root mean square correlation over all N ⋅ (N − 1)/2 pairs.
References
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Journal ArticleDOI
Controlling the false discovery rate: a practical and powerful approach to multiple testing
Yoav Benjamini,Yosef Hochberg +1 more
TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Journal ArticleDOI
The control of the false discovery rate in multiple testing under dependency
Yoav Benjamini,Daniel Yekutieli +1 more
TL;DR: In this paper, it was shown that a simple FDR controlling procedure for independent test statistics can also control the false discovery rate when test statistics have positive regression dependency on each of the test statistics corresponding to the true null hypotheses.
Journal ArticleDOI
A direct approach to false discovery rates
TL;DR: The calculation of the q‐value is discussed, the pFDR analogue of the p‐value, which eliminates the need to set the error rate beforehand as is traditionally done, and can yield an increase of over eight times in power compared with the Benjamini–Hochberg FDR method.
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