ArticleJournal of the American Statistical Association2020
Modeling Between-Study Heterogeneity for Improved Replicability in Gene Signature Selection and Clinical Prediction.
Article in Journal of the American Statistical Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Meta-Analysis Based on Nonconvex Regularization.Scientific reports · 2020Pooled it
- Purity Independent Subtyping of Tumors (PurIST) Pancreatic Cancer Classifier: Analytic Validation of a 16-RNA Expression Signature Distinguishing Basal and Classical Subtypes.The Journal of molecular diagnostics : JMD · 2024Trial
- Variable Selection for Fixed and Random Effects in Multilevel Functional Mixed Effects Models.Statistics in medicine · 2026Article
- Multi-task learning for sparsity pattern heterogeneity: statistical and computational perspectives.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2026Article
- A demethylation-driven gene signature predicts prognosis and therapeutic vulnerability in hepatocellular carcinoma.Scientific reports · 2026Article
- Efficient Computation of High-Dimensional Penalized Piecewise Constant Hazard Random Effects Models.Statistics in medicine · 2025Article
- Efficient computation of high-dimensional penalized generalized linear mixed models by latent factor modeling of the random effects.Biometrics · 2024Article
- glmmPen: High Dimensional Penalized Generalized Linear Mixed Models.The R journal · 2023Article
- Hierarchical resampling for bagging in multistudy prediction with applications to human neurochemical sensing.The annals of applied statistics · 2022Article
- Replicability in cancer omics data analysis: measures and empirical explorations.Briefings in bioinformatics · 2022Article
- Protein structure-based gene expression signatures.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Meta-Analyzing Multiple Omics Data With Robust Variable Selection.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
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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 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.