  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 to select gene signatures and
address data heterogeneity. We compare our method to some commonly used
strategies that select gene signatures ignoring between-study heterogeneity. We
provide asymptotic results justifying the performance of our method and
demonstrate its advantage in the presence of heterogeneity through thorough
simulation studies. Lastly, we motivate our method through a case study
subtyping pancreatic cancer patients from four gene expression studies.
