【摘要】

　　Genome-wide data reveal an intricate landscape where gene activities are highly differentiated across diverse spatial areas. These gene actions and interactions play a critical role in the development and function of both normal and abnormal tissues. As a result, understanding spatial heterogeneity of gene networks is key to developing treatments for human diseases. Despite the abundance of recent spatial gene expression data, extracting meaningful information remains a challenge for local gene interaction discoveries. In response, we have developed staNMF, a method that combines a powerful unsupervised learning algorithm, nonnegative matrix factorization (NMF), with a new stability criterion that selects the size of the dictionary. Using staNMF, we generate biologically meaningful Principle Patterns (PP), which provide a novel and concise representation of Drosophila embryonic spatial expression patterns that correspond to pre-organ areas of the developing embryo. Furthermore, we show how this new representation can be used to automatically predict manual annotations, categorize gene expression patterns, and reconstruct the local gap gene network with high accuracy. Finally, we discuss on-going crispr/cas9 knock-out experiments on Drosophila to verify predicted local gene-gene interactions involving gap-genes, and an open-source software that is being built based on SPARK and Fiji.

　　(This talk is based on collaborative work of a multi-disciplinary team (co-lead Erwin Frise) from the Yu group at UC Berkeley, the Celniker group at the Lawrence Berkeley National Lab (LBNL), and the Xu group at Tsinghua Univ.)

 

　　【个人简历】

　　Bin Yu is Chancellor’s Professor in the Departments of Statistics and of Electrical Engineering & Computer Science at the University of California at Berkeley and a former Chair of Statistics at Berkeley. She is founding co-director of the Microsoft Joint Lab at Peking University on Statistics and Information Technology. Her group at Berkeley is engaged in interdisciplinary research with scientists from genomics, neuroscience, and medicine.  In order to solve data problems in these domain areas, her group develops statistics and machine learning algorithms and theory while integrating with quantitative critical thinking.

She is Member of the U.S. National Academy of Sciences and Fellow of the American Academy of Arts and Sciences. She was a Guggenheim Fellow in 2006, an Invited Speaker at ICIAM in 2011 and the Tukey Memorial Lecturer of the Bernoulli Society in 2012. She was President of IMS in 2013-2014. She is a Fellow of IMS, ASA, AAAS and IEEE.