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In the genomic era, the identification of gene signatures associated withdisease is of significant interest. Such signatures are often used to predictclinical outcomes in new patients and aid clinical decision-making. However,recent studies have shown that gene signatures are often not replicable. Thisoccurrence has practical implications regarding the generalizability andclinical applicability of such signatures. To improve replicability, weintroduce a novel approach to select gene signatures from multiple datasetswhose effects are consistently non-zero and account for between-studyheterogeneity. We build our model upon some rank-based quantities, facilitatingintegration over different genomic datasets. A high dimensional penalizedGeneralized Linear Mixed Model (pGLMM) is used to select gene signatures andaddress data heterogeneity. We compare our method to some commonly usedstrategies that select gene signatures ignoring between-study heterogeneity. Weprovide asymptotic results justifying the performance of our method anddemonstrate its advantage in the presence of heterogeneity through thoroughsimulation studies. Lastly, we motivate our method through a case studysubtyping pancreatic cancer patients from four gene expression studies.

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