On the Development of a Local FDR-Based Approach to Testing Two-Way Classified Hypotheses

Sarkar, Santanu; Nandi, Shinjini

doi:10.1007/s13571-020-00247-6

Cited by 2 publications

(1 citation statement)

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“…Now that we know the theoretical framework that can successfully capture the underlying group effect and yield powerful approaches to multiple testing in one-way classified setting, proceeding further towards extending it to produce newer and powerful Lfdr based approaches answering Q1 and Q2 in two-way classified setting would be a worthwhile goal. Some initial progress towards this goal has been made in Nandi, Sarkar and Chen (2021), Sarkar and Nandi (2021). We intend to expand upon it in our future research.…”

Section: Discussionmentioning

confidence: 93%

Local false discovery rate based methods for multiple testing of one-way classified hypotheses

Sarkar

Zhao

2022

Electron. J. Statist.

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This paper continues the line of research initiated in Liu, Sarkar and Zhao (2016) on developing a novel framework for multiple testing of hypotheses grouped in a one-way classified form using hypothesis-specific local false discovery rates (Lfdr's). It is built on an extension of the standard two-class mixture model from single to multiple groups, defining hypothesisspecific Lfdr as a function of the conditional Lfdr for the hypothesis given that it is within an important group and the Lfdr for the group itself and involving a new parameter that measures grouping effect. This definition captures the underlying group structure for the hypotheses belonging to a group more effectively than the standard two-class mixture model. Two new Lfdr based methods, possessing meaningful optimalities, are produced in their oracle forms. One, designed to control false discoveries across the entire collection of hypotheses, is proposed as a powerful alternative to simply pooling all the hypotheses into a single group and using commonly used Lfdr based method under the standard single-group two-class mixture model. The other is proposed as an Lfdr analog of the method of Benjamini and Bogomolov (2014) for selective inference. It controls Lfdr based measure of false discoveries associated with selecting groups concurrently with controlling the average of within-group false discovery proportions across the selected groups. Simulation studies and real-data application show that our proposed methods are often more powerful than their relevant competitors.

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Section: Discussionmentioning

confidence: 93%

Local false discovery rate based methods for multiple testing of one-way classified hypotheses

Sarkar

Zhao

2022

Electron. J. Statist.

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2dGBH: Two-dimensional group Benjamini–Hochberg procedure for false discovery rate control in two-way multiple testing of genomic data

Yang,

Wang,

Chen

2024

Bioinformatics

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Motivation Emerging omics technologies have introduced a two-way grouping structure in multiple testing, as seen in single-cell omics data, where the features can be grouped by either genes or cell types. Traditional multiple testing methods have limited ability to exploit such two-way grouping structure, leading to potential power loss. Results We propose a new two-dimensional Group Benjamin-Hochberg (2dGBH) procedure to harness the two-way grouping structure in omics data, extending the traditional one-way adaptive GBH procedure. Using both simulated and real datasets, we show that 2dGBH effectively controls the false discovery rate across biologically relevant settings, and it is more powerful than the BH or q-value procedure and more robust than the one-way adaptive GBH procedure. Availability and implementation 2dGBH is available as an R package at: https://github.com/chloelulu/tdGBH. The analysis code and data are available at: https://github.com/chloelulu/tdGBH-paper. Supplementary information Supplementary data are available at Bioinformatics online.

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On the Development of a Local FDR-Based Approach to Testing Two-Way Classified Hypotheses

Cited by 2 publications

References 3 publications

Local false discovery rate based methods for multiple testing of one-way classified hypotheses

Local false discovery rate based methods for multiple testing of one-way classified hypotheses

2dGBH: Two-dimensional group Benjamini–Hochberg procedure for false discovery rate control in two-way multiple testing of genomic data

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