B-SCALING: A NOVEL NONPARAMETRIC DATA FUSION METHOD

• 成果类型: Article
• 署名作者: Liu, Yiwen; Sun, Xiaoxiao; Zhong, Wenxuan; Li, Bing
• 署名单位: University of Arizona; University System of Georgia; University of Georgia; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1537
• 发表日期: 2022
• 页码: 1292-1312
• 关键词: integrative analysis; cancer; epigenetics; DISCOVERY

摘要

Very often for the same scientific question, there may exist different techniques or experiments that measure the same numerical quantity. Historically, various methods have been developed to exploit the information within each type of data independently. However, statistical data fusion methods that could effectively integrate multisource data under a unified framework are lacking. In this paper we propose a novel data fusion method, called Bscaling, for integrating multisource data. Consider K measurements that are generated from different sources but measure the same latent variable through some linear or nonlinear ways. We seek to find a representation of the latent variable, named B-mean, which captures the common information contained in the K measurements while taking into account the nonlinear mappings between them and the latent variable. We also establish the asymptotic property of the B-mean and apply the proposed method to integrate multiple histone modifications and DNA methylation levels for characterizing epigenomic landscape. Both numerical and empirical studies show that B-scaling is a powerful data fusion method with broad applications.