Fwd: Yinglei Lai 报告 (5月11日 周五下午4点,思源楼705)

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Yong Wang

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From: 万林 <lw...@amss.ac.cn>
Date: 2018-05-08 0:20 GMT+08:00
Subject: Yinglei Lai 报告 (5月11日 周五下午4点,思源楼705)
To: 李雷 <li...@amss.ac.cn>, 张世华 <z...@amss.ac.cn>, 王勇 <yw...@amss.ac.cn>, lywu <ly...@amss.ac.cn>, jh zhang <z...@amt.ac.cn>


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报告题目: Detecting concordance and discordance changes among a series of
large-scale data sets

报告人: Yinglei Lai, Professor
       George Washington University
时间: 2018年5月11日 星期五 16:00-17:00 地点: 思源楼 S705 摘要: With the current microarray and RNA sequencing technologies, two-sample genome-wide expression data have been increasingly collected in biological and medical studies. Differential expression analysis and gene set enrichment analysis have been frequently conducted. Integrative analysis can be conducted when multiple data sets are available. In practice, concordant and discordant molecular behaviors among a series of data sets can be of biological and clinical interest. There is still a lack of statistical methods for these types of integrative analysis. We have proposed a mixture model based approach to the integrative analysis of multiple large-scale two-sample expression data sets. Since the mixture model is based on the transformed differential expression test P-values (z-scores), it is generally applicable to the expression data generated by either microarray or RNA sequencing platforms. The mixture model is simple with three normal distribution components for each data set to represent down-regulation, up-regulation and no differential expression. However, when the number of data sets increases, the model parameter space increases exponentially due to the component combination from different data sets. To achieve a concordant and discordant integrative analysis for a series of data sets, we have introduced two model reduction strategies.
We demonstrate our methods on the recent TCGA RNA sequencing data.  To
illustrate a concordant integrative analysis, we apply our method to a
series of data sets collected for studying two closely related types
of cancer.  To illustrate a discordant integrative analysis, we apply
our method to a series of data sets collected for studying different
types of cancer.  Interesting disease-related pathways can be detected
by our integrative analysis approach.
报告人简介:
Dr. Yinglei Lai is Professor of Statistics in the Department of
Statistics at the George Washington University.  His research interest
is to develop statistical and computational methods in bioinformatics,
computational biology and biostatistics.  He received his B.S. in
Information & Computation Sciences and Business Administration from
the University of Science and Technology of China in 1999.  Dr. Lai
received his Ph.D. in Applied Mathematics (Computational Biology) from
the University of Southern California in 2003.  After his postdoctoral
training at Yale University School of Medicine, he joined as a faculty
member in the Department of Statistics at the George Washington
University in 2004.



--
Yong WANG
Academy of Mathematics & Systems Science
Chinese Academy of Sciences
Beijing, 100080, China
Tel: (86-10)82541372(O)
E-mail: yw...@amss.ac.cn
http://wanglab.amss.ac.cn
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