Abstract B24: High-dimensional genomic data integration and bias correction using MANCIE

Abstract

Abstract Most genomic and epigenomic experimental data are presented as high-dimensional matrices. Integrative analysis of such high-dimensional genomic data is challenging due to noises and biases in the high-throughput experiments from different platforms. We present MANCIE (Matrix Analysis and Normalization by Concordant Information Enhancement), a computational method for integrating two genomic datasets based on a Bayesian supported principal component analysis (PCA) approach. We demonstrate that data integration using MANCIE can reduce biases and improve the identification of tissue specificity from the Encyclopedia of DNA Elements (ENCODE) data, improve prognostic prediction from The Cancer Genome Atlas (TCGA) data, and improve the identification of genetic correlation in the Cancer Cell Line Encyclopedia (CCLE) data. MANCIE has broad applications in genomic and epigenomic data analysis in cancer research. Citation Format: Chongzhi Zang, Tao Wang, Ke Deng, Bo Li, Sheng'en Hu, Qian Qin, Tengfei Xiao, Shihua Zhang, Clifford A. Meyer, Housheng Hansen He, Myles Brown, Jun S. Liu, Yang Xie, Xiaole Shirley Liu. High-dimensional genomic data integration and bias correction using MANCIE. [abstract]. In: Proceedings of the AACR Special Conference on Chromatin and Epigenetics in Cancer; Sep 24-27, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2016;76(2 Suppl):Abstract nr B24.