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

Multi‐subgroup gene screening using semi‐parametric hierarchical mixture models and the... IntroductionScreening of genes associated with a phenotype of interest is one of fundamental elements in many genomic studies to understand biological mechanisms. Standard statistical analysis for gene screening is to apply multiple tests regarding the association of individual genes with the phenotypic variable (Speed, ; McLachlan et al., 2004; Simon et al., ; Mayer, ). In such an analysis, it is often the case that there are some important subgroups of samples or conditions and one is interested in whether the profile of association with the phenotype is different across the subgroups at the gene level. Distinguishing genes whose association profiles are similar from those different across subgroups may facilitate deeper understanding of the biological mechanisms across subgroups. Typically, the difference in association profiles across subgroups is investigated via a regression model that associates the phenotypic variable with covariates representing genes, subgroups, and their interactions, where some parametric form for covariates’ effects (e.g., linear form) is specified to capture differential association profiles (Speed, ; McLachlan et al., 2004; Simon et al., ; Mayer, ).A motivating example of this type of analysis is from a gene screening study to associate baseline expressions of tens of thousands of genes measured by http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Wiley

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

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Publisher
Wiley
Copyright
© 2018, The International Biometric Society
ISSN
0006-341X
eISSN
1541-0420
D.O.I.
10.1111/biom.12716
Publisher site
See Article on Publisher Site

Abstract

IntroductionScreening of genes associated with a phenotype of interest is one of fundamental elements in many genomic studies to understand biological mechanisms. Standard statistical analysis for gene screening is to apply multiple tests regarding the association of individual genes with the phenotypic variable (Speed, ; McLachlan et al., 2004; Simon et al., ; Mayer, ). In such an analysis, it is often the case that there are some important subgroups of samples or conditions and one is interested in whether the profile of association with the phenotype is different across the subgroups at the gene level. Distinguishing genes whose association profiles are similar from those different across subgroups may facilitate deeper understanding of the biological mechanisms across subgroups. Typically, the difference in association profiles across subgroups is investigated via a regression model that associates the phenotypic variable with covariates representing genes, subgroups, and their interactions, where some parametric form for covariates’ effects (e.g., linear form) is specified to capture differential association profiles (Speed, ; McLachlan et al., 2004; Simon et al., ; Mayer, ).A motivating example of this type of analysis is from a gene screening study to associate baseline expressions of tens of thousands of genes measured by

Journal

BiometricsWiley

Published: Jan 1, 2018

Keywords: ; ; ; ; ;

References

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