Promoting similarity of model sparsity structures in integrative analysis of cancer genetic data

Yuan Huang, Jin Liu, Huangdi Yi, Ben Chang Shia, Shuangge Ma

研究成果: 雜誌貢獻文章同行評審

9 引文 斯高帕斯(Scopus)


In profiling studies, the analysis of a single dataset often leads to unsatisfactory results because of the small sample size. Multi-dataset analysis utilizes information of multiple independent datasets and outperforms single-dataset analysis. Among the available multi-dataset analysis methods, integrative analysis methods aggregate and analyze raw data and outperform meta-analysis methods, which analyze multiple datasets separately and then pool summary statistics. In this study, we conduct integrative analysis and marker selection under the heterogeneity structure, which allows different datasets to have overlapping but not necessarily identical sets of markers. Under certain scenarios, it is reasonable to expect some similarity of identified marker sets – or equivalently, similarity of model sparsity structures – across multiple datasets. However, the existing methods do not have a mechanism to explicitly promote such similarity. To tackle this problem, we develop a sparse boosting method. This method uses a BIC/HDBIC criterion to select weak learners in boosting and encourages sparsity. A new penalty is introduced to promote the similarity of model sparsity structures across datasets. The proposed method has a intuitive formulation and is broadly applicable and computationally affordable. In numerical studies, we analyze right censored survival data under the accelerated failure time model. Simulation shows that the proposed method outperforms alternative boosting and penalization methods with more accurate marker identification. The analysis of three breast cancer prognosis datasets shows that the proposed method can identify marker sets with increased similarity across datasets and improved prediction performance.
頁(從 - 到)509-559
期刊Statistics in Medicine
出版狀態已發佈 - 2月 10 2017

ASJC Scopus subject areas

  • 流行病學
  • 統計與概率


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