Modeling Between-Study Heterogeneity for Improved Replicability in Gene Signature Selection and Clinical Prediction.

ABSTRACT: In the genomic era, the identification of gene signatures associated with disease is of significant interest. Such signatures are often used to predict clinical outcomes in new patients and aid clinical decision-making. However, recent studies have shown that gene signatures are often not replicable. This occurrence has practical implications regarding the generalizability and clinical applicability of such signatures. To improve replicability, we introduce a novel approach to select gene signatures from multiple datasets whose effects are consistently non-zero and account for between-study heterogeneity. We build our model upon some rank-based quantities, facilitating integration over different genomic datasets. A high dimensional penalized Generalized Linear Mixed Model (pGLMM) is used t

SUBMITTER: Rashid NU 

PROVIDER: S-EPMC7528965 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature