July 2019 PMID 31366926

Fast and covariate-adaptive method amplifies detection power in large-scale multiple hypothesis testing.

Martin J Zhang, Fei Xia, James Zou

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Mentions

Peak: 2 in Aug 2019

across 2 sources since Aug 2019

Extended reach

1,854

potential audience

Total likes

Video views

242

Mentions per month

Aug 2019 – Apr 2025

Where it's discussed

X / Twitter2 posts
YouTube1 videos

AI synopsis

What people are saying

The discussions show enthusiasm for the new method, with posts mentioning its ability to increase discoveries (e.g., 'finding 32% more associations') and its speed and flexibility. The tone suggests a recognition of its significance, as some posters describe it as a 'game-changer,' while others acknowledge its broad applicability, indicating engaging and impactful conversations.

AgreementModerate agreement

Most discussions acknowledge the value of the proposed method for improving detection in large-scale testing, though some maintain a cautious tone.

InterestHigh interest

The topic appears highly engaging, with posters expressing curiosity about the method's capabilities and applications.

EngagementModerate engagement

Participants reference specific aspects like the adaptive learning process and the potential to discover more associations, showing meaningful involvement.

ImpactModerate impact

Many discussions highlight the method’s potential to transform data analysis practices, suggesting a moderate to high influence across fields.

Abstract in brief

AdaFDR is a new method that enhances the detection of true signals in large-scale hypothesis testing by using additional covariate information, which traditional methods like BH ignore.
It adaptively learns the best p-value thresholds based on covariates, leading to significantly more discoveries, such as finding 32% more associations in eQTL analysis of GTEx data, while still controlling the false discovery rate (FDR).
The method is fast, flexible, capable of handling multi-dimensional covariates, and proven to control false discoveries, making it useful across various data science applications.

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Martin Jinye Zhang

@martinjzhang · X

RT @eQTL_papers: Fast and covariate-adaptive method amplifies detection power in large-scale multiple hypothesis testing. https://t.co/Wp8L…

Aug 2, 2019 · 1 share · 983 followers View post →

eQTLpapers

@eQTL_papers · X

Fast and covariate-adaptive method amplifies detection power in large-scale multiple hypothesis testing. https://t.co/Wp8LsjBK3p

Aug 2, 2019 · 2 likes · 1 share · 626 followers View post →