Open AccessA statistical framework for data integration through graphical models with application to cancer genomics
Author(s) -
Yuping Zhang,
Zhengqing Ouyang,
Hongyu Zhao
Publication year - 2017
Publication title -
annals of applied statistics/the annals of applied statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.674
H-Index - 75
eISSN - 1941-7330
pISSN - 1932-6157
DOI - 10.1214/16-aoas998
Subject(s) - genomics , computer science , graphical model , computational biology , biological data , data type , data integration , systems biology , biological network , statistical model , data science , data mining , machine learning , bioinformatics , biology , genome , genetics , gene , programming language
Recent advances in high-throughput biotechnologies have generated var-ious types of genetic, genomic, epigenetic, transcriptomic and proteomic data across different biological conditions. It is likely that integrating data from diverse experiments may lead to a more unified and global view of biolog-ical systems and complex diseases. We present a coherent statistical frame-work for integrating various types of data from distinct but related biological conditions through graphical models. Specifically, our statistical framework is designed for modeling multiple networks with shared regulatory mech-anisms from heterogeneous high-dimensional datasets. The performance of our approach is illustrated through simulations and its applications to cancer genomics.