A semiparametric mixture method for local false discovery rate estimation from multiple studies

Seok Oh Jeong, Dongseok Choi, Woncheol Jang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Antineutrophil cytoplasmic antibody associated vasculitis (AAV) is ex-tremely heterogeneous in clinical presentation and involves multiple organ systems. While the clinical presentation of AAV is diverse, we hypothe-sized that all AAV share common pathways and tested the hypothesis based on three different microarray studies of peripheral leukocytes, sinus and orbital inflammation disease. For the hypothesis testing we developed a two-component semiparametric mixture model to estimate the local false discovery rates from the p-values of three studies. The two pillars of the proposed approach are Efron’s empirical null principle and log-concave density estimation for the alternative distribution. Our method outperforms other existing methods, in particular when the proportion of null is not that high. It is robust against the misspecification of alternative distribution. A unique feature of our method is that it can be extended to compute the local false discovery rates by combining multiple lists of p-values.

Original languageEnglish (US)
Pages (from-to)1242-1257
Number of pages16
JournalAnnals of Applied Statistics
Volume14
Issue number3
DOIs
StatePublished - 2020

Keywords

  • False discovery rate
  • Log concave
  • Microarray
  • Mixture model
  • Next generation sequencing data

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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