title: Matrix factorization methods for integrative cancer genomics.
abstract: With the rapid development of high-throughput sequencing technologies, many groups are generating multi-platform genomic profiles (e.g., DNA methylation and gene expression) for their biological samples. This activity has generated a huge number of so-called "multidimensional genomic datasets," providing unique opportunities and challenges to study coordination among different regulatory levels and discover underlying combinatorial patterns of cellular systems. We summarize a matrix factorization framework to address the challenge of integrating multiple genomic datasets, as well as a semi-supervised variant of the method that can incorporate prior knowledge. The basic idea is to project the different kinds of genomic data onto a common coordinate system, wherein genetic variables that are strongly correlated in a subset of samples form a multidimensional module. In the context of cancer biology, such modules reveal perturbed pathways and clinically distinct patient subgroups that would have been overlooked with only a single type of data. In summary, the matrix factorization framework can uncover associations between distinct layers of cellular activity and explain their biological implications in multidimensional data.
authors.affiliation: National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, 55, Zhongguancun East Road, Beijing, 100190, China.
authors.forename: Shihua
authors.initials: S
authors.lastname: Zhang
authors.forename: Xianghong Jasmine
authors.initials: XJ
authors.lastname: Zhou
mesh_terms: D019295:Computational Biology / Q000379:methods*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D023281:Genomics / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism; D010051:Ovarian Neoplasms / Q000235:genetics; D025941:Protein Interaction Mapping / Q000379:methods