University of California at Los Angeles, 2007, PhD (Statistics)
University of California at Los Angeles, 2004, MS (Statistics)
Peking University, Beijing, 2002, BS (Statistics)
Develop statistical/computational methods and software packages for different types of omic data, including array or sequencing data for germline/somatic point mutations, copy number alterations, DNA methylation, and gene expression. Integrating multiple types of omic data from tumor samples to study intra-tumor heterogeneity and tumor microenvironments. Develop statistical methods for high-dimensional data and graphical models.
RNA-seq and allele-specific expression (ASE) analysis. RNA-seq data provide a novel features for gene expression study, namely the allele-specific gene expression. We have developed statistical methods to study ASE using RNA-seq to detect gene expression quantitative trait loci (eQTL), and to dissect the genetic and parent of origin basis of ASE in mouse studies. We are currently studying statistical methods to dissect genetic and parent of origin basis of ASE in human studies, as well as ASE in tumor samples.
Intra-tumor heterogeneity. Intra-tumor heterogeneity refers to the fact that the tumor cells are not homogeneous, and they may form different subclones. We are integrating different types of omic data, including Somatic Copy Number Methylation (SCNA), somatic point mutations, DNA methylation and gene expression to study the intra-tumor heterogeneity. We will also develop new methods to integrating omic data from bulk tumor sample and single cells to infer intra-tumor heterogeneity.
Tumor microenvironment. Each tumor sample often includes both tumor and non-tumor cells, which include stromal cells, infiltrating immune cells, among other types of cells. We aim to dissect such cell type composition using omic data and to understand the interplay between tumor cells and other types of cells.
High dimensional graphical model. We have developed statistical methods to infer the skeleton of high dimensional Directed Acyclic Graphs (DAGs) using gene expression data. We are currently working on new methods that can estimate DAGs using both gene expression and eQTL information.
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