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Academy of Mathematics and Systems Science, CAS
Colloquia & Seminars

Speaker:

Professor Yinglei Lai, George Washington University

Inviter:  
Title:
Detecting concordance and discordance changes among a series of large-scale data sets
Time & Venue:
2018.5.11 16:00-17:00 N705
Abstract:
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.

 

 

 

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