|Modeling Between-Study Heterogeneity for Improved Replicability in Gene Signature Selection and Clinical Prediction.
|Year of Publication
|Rashid, Naim U., Quefeng Li, Jen Jen Yeh, and Joseph G. Ibrahim
|J Am Stat Assoc
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.
|J Am Stat Assoc
|Modeling between-study heterogeneity for improved replicability in gene signature selection and clinical prediction.
|PubMed Central ID
|R01 GM105785 / GM / NIGMS NIH HHS / United States
R01 CA199064 / CA / NCI NIH HHS / United States
R01 CA193650 / CA / NCI NIH HHS / United States
P30 CA016086 / CA / NCI NIH HHS / United States
R01 GM070335 / GM / NIGMS NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States