Integrative learning of structured high‐dimensional data from multiple datasets. (8th November 2022)

• Record Type: Journal Article
• Authors: Chang, Changgee; Dai, Zongyu; Oh, Jihwan; Long, Qi
• Is Part Of: Statistical analysis and data mining. Volume 16:Number 2(2023)
• Page Start: 120
• Page End: 134
• Publication Date: 2022-11-08
• Subjects: high‐dimensional data -- horizontally partitioned data -- integrative learning -- knowledge‐guided learning -- network‐based penalty; Data mining -- Statistical methods -- Periodicals; 006.312
• DOI: 10.1002/sam.11601
• Languages: English
• ISSNs: 1932-1864

Abstract

Abstract: Integrative learning of multiple datasets has the potential to mitigate the challenge of small n $$ n $$ and large p $$ p $$ that is often encountered in analysis of big biomedical data such as genomics data. Detection of weak yet important signals can be enhanced by jointly selecting features for all datasets. However, the set of important features may not always be the same across all datasets. Although some existing integrative learning methods allow heterogeneous sparsity structure where a subset of datasets can have zero coefficients for some selected features, they tend to yield reduced efficiency, reinstating the problem of losing weak important signals. We propose a new integrative learning approach which can not only aggregate important signals well in homogeneous sparsity structure, but also substantially alleviate the problem of losing weak important signals in heterogeneous sparsity structure. Our approach exploits a priori known graphical structure of features and encourages joint selection of features that are connected in the graph. Integrating such prior information over multiple datasets enhances the power, while also accounting for the heterogeneity across datasets. Theoretical properties of the proposed method are investigated. We also demonstrate the limitations of existing approaches and the superiority of our method using a simulation study and analysis of gene expression data from ADNI.