American Statistical Association
The recent large-scale technologies like microarrays and RNA-seq allow us to collect genome-wide expression profiles for biomedical studies. Genes showing significant differential expression are potentially important biomarkers. A gene set enrichment analysis enables us to identify groups of genes (e.g. pathways) showing coordinate differential expression. Genes and gene sets showing consistent behavior in two related studies can be of great biological interest. However, since the sample sizes are usually small but the numbers of genes are large, it is difficult to identify truly differentially expressed genes and determine whether a gene or a gene set behaves concordantly in two related studies. We have recently shown that the mixture model based approach can be an efficient solution for the concordant analysis of differential expression in two two-sample large-scale expression data sets. The advantage of the mixture model based approach is that the probability of a particular behavior (up-regulation or down-regulation) can be estimated for a given gene. Thus, it is feasible to address how likely this gene shows a concordant behavior in both data sets. In our recent study, we also extend this approach for the concordant gene set enrichment analysis.
Yinglei Lai received his B.S. in Information & Computation Sciences and Business Administration from the University of Science and Technology of China in 1999. Dr. Lai subsequently 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 Assistant Professor in the Department of Statistics at the George Washington University in 2004 and was promoted to Associate Professor of Statistics in 2010. Dr. Lai's research interest is to develop statistical methods for bioinformatics, computational biology and statistical genetics.
|Date:||Thursday, April 14, 2011|
|Time:||4:00 - 5:00 P.M.|
Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Computer Lab
6th Floor - Room 656
New York, New York