Capturing the severity of type II errors in high-dimensional multiple testing

He, Li; Sarkar, Santanu; Zhao, Zhigen

doi:10.1016/j.jmva.2015.08.005

Cited by 14 publications

(17 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The empirical Bayes procedure was first introduced by Robbins (1951Robbins ( , 1956, and is also known as a nonparametric empirical Bayes procedure because the prior is completely unspecified. Recently, Sun and Cai (2007) and He, Sarkar and Zhao (2012) constructed optimal nonparametric empirical Bayes multiple testing procedures in the normal mean setting. In our study, the observation follows a binomial-binomial model.…”

Section: Find the Maximum J Such Thatmentioning

confidence: 99%

See 1 more Smart Citation

An empirical Bayes testing procedure for detecting variants in analysis of next generation sequencing data

Zhao

Wang

Wei³

2013

Ann. Appl. Stat.

Self Cite

View full text Add to dashboard Cite

Because of the decreasing cost and high digital resolution, nextgeneration sequencing (NGS) is expected to replace the traditional hybridization-based microarray technology. For genetics study, the first-step analysis of NGS data is often to identify genomic variants among sequenced samples. Several statistical models and tests have been developed for variant calling in NGS study. The existing approaches, however, are based on either conventional Bayesian or frequentist methods, which are unable to address the multiplicity and testing efficiency issues simultaneously. In this paper, we derive an optimal empirical Bayes testing procedure to detect variants for NGS study. We utilize the empirical Bayes technique to exploit the acrosssite information among many testing sites in NGS data. We prove that our testing procedure is valid and optimal in the sense of rejecting the maximum number of nonnulls while the Bayesian false discovery rate is controlled at a given nominal level. We show by both simulation studies and real data analysis that our testing efficiency can be greatly enhanced over the existing frequentist approaches that fail to pool and utilize information across the multiple testing sites.

show abstract

Section: Find the Maximum J Such Thatmentioning

confidence: 99%

“…,Wei et al (2009), Wang, Wei andSun (2010),He, Sarkar and Zhao (2012), Wei (2011), Xie et al (2011)].…”

mentioning

confidence: 99%

An empirical Bayes testing procedure for detecting variants in analysis of next generation sequencing data

Zhao

Wang

Wei³

2013

Ann. Appl. Stat.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Another example arises from genome-wide association studies (GWAS), where prior data or genomic knowledge, such as prioritized subsets (Lin and Lee, 2012), allele frequencies (Lin et al, 2014) and expression quantitative trait loci information (Li et al, 2013), can often help to assess the scientific plausibility of significant associations. To incorporate such information in the analysis, a useful strategy is to up-weight the gains for the discoveries in preselected genomic regions by modifying the power functions in respective testing units (P˜ena et al, 2011; Sun et al, 2015; He et al, 2015). We assume in this paper that the weights have been pre-specified by the investigator.…”

Section: Introductionmentioning

confidence: 99%

Weighted False Discovery Rate Control in Large-Scale Multiple Testing

Basu

Cai

Das

et al. 2018

Journal of the American Statistical Association

View full text Add to dashboard Cite

The use of weights provides an effective strategy to incorporate prior domain knowledge in large-scale inference. This paper studies weighted multiple testing in a decisiontheoretic framework. We develop oracle and data-driven procedures that aim to maximize the expected number of true positives subject to a constraint on the weighted false discovery rate. The asymptotic validity and optimality of the proposed methods are established. The results demonstrate that incorporating informative domain knowledge enhances the interpretability of results and precision of inference. Simulation studies show that the proposed method controls the error rate at the nominal level, and the gain in power over existing methods is substantial in many settings. An application to a genome-wide association study is discussed.

show abstract

“…A Bayesian approach can produce powerful method of controlling a rate of false discoveries when testing multiple hypotheses (He et al (2015), Sun & Cai (2007), , Efron & Tibshirani (2002), Tang & Zhang (2007), Sarkar et al (2008), and Newton et al (2004)).…”

Section: Introductionmentioning

confidence: 99%

A new approach to multiple testing of grouped hypotheses

Liu

Sarkar

Zhao

2016

Journal of Statistical Planning and Inference

Self Cite

View full text Add to dashboard Cite

A twofold loop testing algorithm (TLTA) is proposed for testing grouped hypotheses controlling false discoveries. It is constructed by decomposing a posterior measure of false discoveries across all hypotheses into within-and between-group components, allowing a portion of the overall FDR level to be used to maintain control over within-group false discoveries. Numerical calculations performed under certain model assumption for the hidden states of the within-group hypotheses show its superior performance over its competitors that ignore the group structure, especially when only a few of the groups contain the signals, as expected in many modern applications. We offer data-driven version of the TLTA by estimating the parameters using EM algorithms and provide simulation evidence of its favorable performance relative to these competitors. Real data applications have also produced encouraging results for the TLTA.

show abstract

Capturing the severity of type II errors in high-dimensional multiple testing

Cited by 14 publications

References 12 publications

An empirical Bayes testing procedure for detecting variants in analysis of next generation sequencing data

An empirical Bayes testing procedure for detecting variants in analysis of next generation sequencing data

Weighted False Discovery Rate Control in Large-Scale Multiple Testing

A new approach to multiple testing of grouped hypotheses

Contact Info

Product

Resources

About