Multi-subgroup gene screening using semi-parametric hierarchical mixture models and the optimal discovery procedure: Application to a randomized clinical trial in multiple myeloma

Biometrics
Shigeyuki MatsuiJohn Crowley

Abstract

This article proposes an efficient approach to screening genes associated with a phenotypic variable of interest in genomic studies with subgroups. In order to capture and detect various association profiles across subgroups, we flexibly estimate the underlying effect size distribution across subgroups using a semi-parametric hierarchical mixture model for subgroup-specific summary statistics from independent subgroups. We then perform gene ranking and selection using an optimal discovery procedure based on the fitted model with control of false discovery rate. Efficiency of the proposed approach, compared with that based on standard regression models with covariates representing subgroups, is demonstrated through application to a randomized clinical trial with microarray gene expression data in multiple myeloma, and through a simulation experiment.

References

Jul 29, 2003·Proceedings of the National Academy of Sciences of the United States of America·John D Storey, Robert Tibshirani
Mar 10, 2006·The New England Journal of Medicine·Bart BarlogieJohn Crowley
Jan 8, 2009·BMC Bioinformatics·Jing CaoMichael A White
May 19, 2010·Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology·Bart BarlogieJohn Crowley
Oct 5, 2011·Statistics in Medicine·Hisashi Noma, Shigeyuki Matsui
Aug 29, 2012·Clinical Cancer Research : an Official Journal of the American Association for Cancer Research·Shigeyuki MatsuiJohn Crowley
Mar 3, 2015·Journal of the American Statistical Association·Lu TianRobert Tibshirani
Jun 19, 2015·Statistics in Medicine·Yicong LiuBingshu E Chen
Mar 22, 2016·Biometrics·Yuanyuan Shen, Tianxi Cai

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Citations

Apr 16, 2020·Biometrics·Shonosuke Sugasawa, Hisashi Noma
Sep 12, 2018·European Journal of Human Genetics : EJHG·Takahiro OtaniTatsuhiko Tsunoda
Apr 2, 2019·Statistics in Medicine·Takanori KawabataShigeyuki Matsui

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