Unveiling the Mysteries in Spatial Gene Expression
Time: 3 p.m.
Date: May 2, 2016
Location: 209 W. 18th Avenue Building, Room 160
Invitees: Faculty, staff, students
Bin Yu

Bin Yu

The Department of Statistics will present the Chhotey Lal and Mohra Devi Rustagi Memorial Lecture featuring Bin Yu, PhD.

Yu is Chancellor’s Professor in the Departments of Statistics and Electrical Engineering & Computer Science at the University of California at Berkeley. Her current research interests focus on statistics and machine learning theory, methodologies, and algorithms for solving high-dimensional data problems. Her group is engaged in interdisciplinary research with scientists from genomics, neuroscience, and remote sensing. She is a Member of the National Academy of Sciences and Fellow of the American Academy of Arts and Sciences. She was a Guggenheim Fellow in 2006, and President of IMS (Institute of Mathematical Statistics) in 2013-2014. She is a Fellow of IMS, ASA, IEEE and AAAS. She has served or is serving on numerous journal editorial boards including those of JMLR, Annals of Statistics, and JASA. She is on SAB of IPAM and BOT of ICERM.

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. An open-source software is also 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 (statistics) at UC Berkeley, the Celniker group (biology) at the Lawrence Berkeley National Lab (LBNL), and the Xu group (computer science) at Hsinghua Univ.

A reception will follow immediately afterwards at the Math Tower 7th floor lounge.

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