Powerful p-value combination methods to detect incomplete association

Abstract Meta-analyses increase statistical power by combining statistics from multiple studies. Meta-analysis methods have mostly been evaluated under the condition that all the data in each study have an association with the given phenotype. However, specific experimental conditions in each study...

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Autores principales:	Sora Yoon, Bukyung Baik, Taesung Park, Dougu Nam
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/855cb6e277fa4760b73d3ae5f6fd7226
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spelling	oai:doaj.org-article:855cb6e277fa4760b73d3ae5f6fd72262021-12-02T17:04:36ZPowerful p-value combination methods to detect incomplete association10.1038/s41598-021-86465-y2045-2322https://doaj.org/article/855cb6e277fa4760b73d3ae5f6fd72262021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86465-yhttps://doaj.org/toc/2045-2322Abstract Meta-analyses increase statistical power by combining statistics from multiple studies. Meta-analysis methods have mostly been evaluated under the condition that all the data in each study have an association with the given phenotype. However, specific experimental conditions in each study or genetic heterogeneity can result in “unassociated statistics” that are derived from the null distribution. Here, we show that power of conventional meta-analysis methods rapidly decreases as an increasing number of unassociated statistics are included, whereas the classical Fisher’s method and its weighted variant (wFisher) exhibit relatively high power that is robust to addition of unassociated statistics. We also propose another robust method based on joint distribution of ordered p-values (ordmeta). Simulation analyses for t-test, RNA-seq, and microarray data demonstrated that wFisher and ordmeta, when only a small number of studies have an association, outperformed existing meta-analysis methods. We performed meta-analyses of nine microarray datasets (prostate cancer) and four association summary datasets (body mass index), where our methods exhibited high biological relevance and were able to detect genes that the-state-of-the-art methods missed. The metapro R package that implements the proposed methods is available from both CRAN and GitHub ( http://github.com/unistbig/metapro ).Sora YoonBukyung BaikTaesung ParkDougu NamNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sora Yoon Bukyung Baik Taesung Park Dougu Nam Powerful p-value combination methods to detect incomplete association
description	Abstract Meta-analyses increase statistical power by combining statistics from multiple studies. Meta-analysis methods have mostly been evaluated under the condition that all the data in each study have an association with the given phenotype. However, specific experimental conditions in each study or genetic heterogeneity can result in “unassociated statistics” that are derived from the null distribution. Here, we show that power of conventional meta-analysis methods rapidly decreases as an increasing number of unassociated statistics are included, whereas the classical Fisher’s method and its weighted variant (wFisher) exhibit relatively high power that is robust to addition of unassociated statistics. We also propose another robust method based on joint distribution of ordered p-values (ordmeta). Simulation analyses for t-test, RNA-seq, and microarray data demonstrated that wFisher and ordmeta, when only a small number of studies have an association, outperformed existing meta-analysis methods. We performed meta-analyses of nine microarray datasets (prostate cancer) and four association summary datasets (body mass index), where our methods exhibited high biological relevance and were able to detect genes that the-state-of-the-art methods missed. The metapro R package that implements the proposed methods is available from both CRAN and GitHub ( http://github.com/unistbig/metapro ).
format	article
author	Sora Yoon Bukyung Baik Taesung Park Dougu Nam
author_facet	Sora Yoon Bukyung Baik Taesung Park Dougu Nam
author_sort	Sora Yoon
title	Powerful p-value combination methods to detect incomplete association
title_short	Powerful p-value combination methods to detect incomplete association
title_full	Powerful p-value combination methods to detect incomplete association
title_fullStr	Powerful p-value combination methods to detect incomplete association
title_full_unstemmed	Powerful p-value combination methods to detect incomplete association
title_sort	powerful p-value combination methods to detect incomplete association
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/855cb6e277fa4760b73d3ae5f6fd7226
work_keys_str_mv	AT sorayoon powerfulpvaluecombinationmethodstodetectincompleteassociation AT bukyungbaik powerfulpvaluecombinationmethodstodetectincompleteassociation AT taesungpark powerfulpvaluecombinationmethodstodetectincompleteassociation AT dougunam powerfulpvaluecombinationmethodstodetectincompleteassociation
_version_	1718381858728706048

Powerful p-value combination methods to detect incomplete association

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