Cells are complex systems with multiple layers of organization that interact and influence each other. The precise coordination among epigenetic status, transcriptions, translations, transportation, and metabolic reactions are essential in maintaining the function and robustness of the cellular systems. The emerging multi-dimensional genomics data presented unprecedented opportunities to study the cross-layer coordination, but also poses new challenges in data analysis.  Here, we propose a joint matrix factorization framework as well as a sparse network-regularized version to address these challenges. We applied these methods to the genomics datasets of 385 ovarian cancer samples from the TCGA project. We performed extensive validation tests based on known functional gene set (GO, KEGG and GeneRIF pathway), overlap analysis for different level data, network and pathway analysis, and clinical association analysis. Our study provides a powerful analytical framework for uncovering the biological patterns and implications across multi-dimensional `omic' data.
 
Introduction of the speaker:    张世华博士于2008年7月毕业于中国科杏悦数学与系统科学研究院，并留所工作任助理研究员。于2008年7月到2010年12月在美国南加州大学计算生物学中心从事博士后研究；2011年10月—12月访问美国加州洛杉矶分校数学与应用数学研究所👱🏻‍♀️；2012年2月访问新加坡国立大学数学系👩🏿‍💻👩🏼‍🎤。目前主要研究领域为计算生物学与网络科学。