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Nam, Dougu
Bioinformatics Lab
Research Interests
  • Gene network, pathway analysis, biclustering, disease classification

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Powerful p-value combination methods to detect incomplete association

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Title
Powerful p-value combination methods to detect incomplete association
Author
Yoon, SoraBaik, BukyungPark, TaesungNam, Dougu
Issue Date
2021-03
Publisher
NATURE RESEARCH
Citation
SCIENTIFIC REPORTS, v.11, no.1, pp.6980
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).
URI
https://scholarworks.unist.ac.kr/handle/201301/52790
URL
https://www.nature.com/articles/s41598-021-86465-y
DOI
10.1038/s41598-021-86465-y
ISSN
2045-2322
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BIO_Journal Papers
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