The Department of Biomedical Informatics and Data Science (DBIDS) invites you to join us for our PowerTalks Seminar Series featuring Chang Su, Ph.D., Assistant Professor of Biostatistics and Bioinformatics at Emory University Rollins School of Public Health. Her presentation will be entitled "Statistical Inference of Cell-Type-Specific Gene Co-expression Networks with Single-Cell and Bulk RNA-seq Data."

Dr. Chang Su is an assistant professor of Biostatistics and Bioinformatics at Emory University. Her main research interest is the development of statistical methods in single-cell genomics and genetics. Her current projects include the analysis of single-cell multi-omics data and genetic data for neurodegenerative, lung, and autoimmune diseases. Her methodological work has been published in leading scientific journals, such as Nature Communications, and leading statistics journals, such as the Journal of the American Statistical Association. Prior to Emory, Dr. Su received her Ph.D. in Biostatistics from Yale University.

Abstract: The inference of gene co-expression from microarray and RNA-sequencing (RNA-seq) data has led to rich insights in biological processes and disease mechanisms. However, the bulk samples analyzed in most studies are a mixture of different cell types. As a result, the inferred co-expressions are confounded by varying cell type compositions across samples and only offer an aggregate view of gene regulations that may be distinct across different cell types. In this talk, we introduce two new statistical methods for inferring cell-type-specific co-expression networks based on two distinct types of RNA-seq data. First, to address the unique opportunity and challenge from the recently developed single-cell RNA-seq technology, we have proposed a novel method named CS-CORE that explicitly accounts for the high sequencing depth variations and measurement errors present in single cell data for estimating and testing cell-type-specific co-expression. When applied to analyze multiple scRNA-seq datasets, CS-CORE identified cell-type-specific co-expressions and differential co-expressions that were more reproducible and/or more enriched for relevant biological pathways than those inferred from existing methods. Moreover, to leverage the rich collection of bulk RNA-seq data from the past 15 years, we have also developed CSNet, a flexible framework to estimate cell-type-specific gene co-expression networks from bulk sample data and investigated theoretical properties of the proposed estimator. When applied to analyze bulk RNA-seq data from Alzheimer’s disease (AD), CSNet identified previously unknown cell-type-specific co-expressions among AD risk genes, suggesting cell-type-specific disease pathology in AD. The general framework in CS-CORE and CSNet can be adopted to integrate single cell and bulk RNA-seq data for more efficient use of the accumulating data in different diseases.