论文题目：Discovery of multi-dimensional modules by integrative analysis of cancer genomic data

作者：Shihua Zhang, Chun-Chi Liu, Wenyuan Li, Hui Shen, Peter W. Laird and Xianghong Jasmine Zhou

摘要:Recent technology has made it possible to simultaneously perform multi-platform genomic profiling (e.g., DNA methylation, and gene expression) of biological samples, resulting in so-called ``multi-dimensional genomic data". Such data provide unique opportunities to study the coordination between regulatory mechanisms on multiple levels. However, integrative analysis of multi-dimensional genomics data for the discovery of combinatorial patterns is currently lacking. Here, we adopt a joint matrix factorization technique to address this challenge. This method projects multiple types of genomic data onto a common coordinate system, in which heterogeneous variables weighted highly in the same projected direction form a multi-dimensional module. Genomic variables in such modules are characterized by significant correlations and likely functional associations. We applied this method to the DNA methylation, gene expression, and microRNA expression data of 385 ovarian cancer samples from the TCGA project. These multi-dimensional modules revealed perturbed pathways that would have been overlooked with only a single type of data, uncovered associations between different layers of cellular activities, and allowed the identification of clinically distinct patient subgroups. Our study provides an useful protocol for uncovering hidden patterns and their biological implications in multi-dimensional ``omic" data.

所属实验室或研究中心：随机复杂结构与数据科学重点实验室、生物信息学研究中心

期刊：Nucleic Acids Research