【学术会议】Dynamic correlation analysis for high-throughput expression data
- 题目: Dynamic correlation analysis for high-throughput expression data
- 报告人: Tianwei Yu , Emory University
- 时间: 4:00 – 5:00 pm, December 14, 2018
- 地点: Boardroom, Dao Yuan Building
Dynamic correlations are pervasive in high-throughput data. Large numbers of gene pairs can change their correlation patterns in response to observed/unobserved changes in physiological states. Finding changes in correlation patterns can reveal important regulatory mechanisms. Currently there is no method that can effectively detect global dynamic correlation patterns in a dataset. Given the challenging nature of the problem, the currently available methods use genes as surrogate measurements of physiological states, which cannot faithfully represent true underlying biological signals. In this study we develop a new method that directly identifies strong latent dynamic correlation signals from the data matrix, named DCA: Dynamic Correlation Analysis. At the center of the method is a new metric for the identification of pairs of variables that are highly likely to be dynamically correlated, without knowing the underlying physiological states that govern the dynamic correlation. We validate the performance of the method with extensive simulations. We applied the method to three real datasets: a single cell RNA-seq dataset, a bulk RNA-seq dataset, and a microarray gene expression dataset. In all three datasets, the method reveals novel latent factors with clear biological meaning, bringing new insights into the data.
Tianwei Yu graduated at the biology department of Tsinghua University in 1997, obtained his master’s degree of biochemistry in 2000 at Tsinghua University, and earned his Ph.D. of statistics at UCLA in 2005. From 2006 till now, Yu teaches in the Department of Biostatistics and Bioinformatics at Emory University, and is now a Tenured Associate Professor. Tianwei’s research orientation includes spectroscopy-based preprocessing of metabolomics data, systems biology, and nonlinear statistical mod