ArticleStatistics in medicine2011
Integrative analysis of multiple cancer prognosis studies with gene expression measurements.
Article in Statistics in medicine, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Integrative analysis of high-dimensional quantile regression with contrasted penalization.Journal of applied statistics · 2025Article
- Functional Concurrent Regression Mixture Models Using Spiked Ewens-Pitman Attraction Priors.Bayesian analysis · 2024Article
- A penalized integrative deep neural network for variable selection among multiple omics datasets.Quantitative biology (Beijing, China) · 2024Article
- Bayesian multi-source regression and monocyte-associated gene expression predict BCL-2 inhibitor resistance in acute myeloid leukemia.NPJ precision oncology · 2021Article
- Integrative analysis from multi-centre studies identifies a function-derived personalized multi-gene signature of outcome in colorectal cancer.Journal of cellular and molecular medicine · 2019Article
- Disease classification via gene network integrating modules and pathways.Royal Society open science · 2019Article
- Article
- Identifying gene-environment interactions incorporating prior information.Statistics in medicine · 2019Article
- Identification of cancer omics commonality and difference via community fusion.Statistics in medicine · 2019Article
- Integrative sparse principal component analysis of gene expression data.Genetic epidemiology · 2017Article
- Promoting similarity of model sparsity structures in integrative analysis of cancer genetic data.Statistics in medicine · 2017Article
- Promoting Similarity of Sparsity Structures in Integrative Analysis with Penalization.Journal of the American Statistical Association · 2017Article
- LINKING LUNG AIRWAY STRUCTURE TO PULMONARY FUNCTION VIA COMPOSITE BRIDGE REGRESSION.The annals of applied statistics · 2016Article
- Similarity of markers identified from cancer gene expression studies: observations from GEO.Briefings in bioinformatics · 2014Article
- Detection of candidate tumor driver genes using a fully integrated Bayesian approach.Statistics in medicine · 2014Article
- Integrative Analysis of Cancer Diagnosis Studies with Composite Penalization.Scandinavian journal of statistics, theory and applications · 2014Article
- Integrative analysis of high-throughput cancer studies with contrasted penalization.Genetic epidemiology · 2014Article
- Integrative analysis of multiple cancer genomic datasets under the heterogeneity model.Statistics in medicine · 2013Article
- Estimation and selection of complex covariate effects in pooled nested case-control studies with heterogeneity.Biostatistics (Oxford, England) · 2013Article
- Group sparse canonical correlation analysis for genomic data integration.BMC bioinformatics · 2013Article
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5 authors.
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Abstract
Although in cancer research microarray gene profiling studies have been successful in identifying genetic variants predisposing to the development and progression of cancer, the identified markers from analysis of single datasets often suffer low reproducibility. Among multiple possible causes, the most important one is the small sample size hence the lack of power of single studies. Integrative analysis jointly considers multiple heterogeneous studies, has a significantly larger sample size, and can improve reproducibility. In this article, we focus on cancer prognosis studies, where the response variables are progression-free, overall, or other types of survival. A group minimax concave penalty (GMCP) penalized integrative analysis approach is proposed for analyzing multiple heterogeneous cancer prognosis studies with microarray gene expression measurements. An efficient group coordinate descent algorithm is developed. The GMCP can automatically accommodate the heterogeneity across multiple datasets, and the identified markers have consistent effects across multiple studies. Simulation studies show that the GMCP provides significantly improved selection results as compared with the existing meta-analysis approaches, intensity approaches, and group Lasso penalized integrative analysis. We apply the GMCP to four microarray studies and identify genes associated with the prognosis of breast cancer.
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