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Ann Appl Stat. 2017 Mar;11(1):161-184. doi: 10.1214/16-AOAS998. Epub 2017 Apr 8.

A STATISTICAL FRAMEWORK FOR DATA INTEGRATION THROUGH GRAPHICAL MODELS WITH APPLICATION TO CANCER GENOMICS.

Author information

1
DEPARTMENT OF STATISTICS, INSTITUTE FOR SYSTEMS GENOMICS, CENTER FOR QUANTITATIVE MEDICINE, INSTITUTE FOR COLLABORATION ON HEALTH, INTERVENTION, AND POLICY, THE CONNECTICUT INSTITUTE FOR THE BRAIN AND COGNITIVE SCIENCES, UNIVERSITY OF CONNECTICUT, STORRS, CONNECTICUT 06269, USA, yuping.zhang@uconn.edu.
2
THE JACKSON LABORATORY FOR GENOMIC MEDICINE, DEPARTMENT OF BIOMEDICAL ENGINEERING, DEPARTMENT OF GENETICS AND GENOME SCIENCES, INSTITUTE FOR SYSTEMS GENOMICS, UNIVERSITY OF CONNECTICUT, FARMINGTON, CONNECTICUT 06030, USA, zhengqing.ouyang@jax.org.
3
DEPARTMENT OF BIOSTATISTICS, YALE SCHOOL OF PUBLIC HEALTH, NEW HAVEN, CONNECTICUT 06510, USA, hongyu.zhao@yale.edu.

Abstract

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.

KEYWORDS:

Cancer genomics; data integration; graphical models

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