Presentation: 2018-19 Norman Breslow Endowed Lecture: Learning From the Transcriptome: Analysis of Single Cell and Bulk RNA Sequence Data
Speaker: Kathryn Roeder, Ph.D., UPMC Professor of Statistics and Life Sciences, and Vice Provost for Faculty, Carnegie Mellon University
Abstract: Quantification of gene expression can be a critical step towards characterizing the etiology of complex diseases. Growth typically involves differentiation from progenitor cells into more specialized descendants, often involving lineages of pure and transitional cells to achieve final form. Recent technology has enabled estimation of gene expression profiles of single cells and these profiles theoretically differentiate pure cell types. What is missing from the analytical toolbox is an efficient technique to classify pure and transitional cells from their profiles. Here I introduce SOUP, for Semi-sOft clUstering with Pure cells.
While there are many strengths to single cell expression, the data tend to be noisy. Hence we propose a method to glean more insight from bulk gene expression. Our objective is to borrow information across multiple measurements of the same tissue per individual, such as multiple regions of the brain, using an empirical Bayes approach to estimate individual- and cell-type-specific gene expression. To illustrate, we estimate gene co-expression networks in specific brain cell types, which are then interpreted in light of genetic findings in autism spectrum disorder (ASD).
About Dr. Roeder: Kathryn Roeder earned her Ph.D. in statistics at Pennsylvania State University, after which she worked at Yale University for the next six years before coming to Carnegie Mellon in 1994. In 1997, Dr. Roeder received the COPSS Presidents’ Award for the outstanding statistician under age 40, as well as the Snedecor Award for outstanding work in statistical applications. In 2013, she received the Janet L. Norwood Award for outstanding achievement by a woman in statistical sciences. Dr. Roeder joined the Departments of Statistics and Data Science & Computational Biology as a faculty member in 2004 to encourage a bridge between statistics, machine learning, genetics and genomics. Dr. Roeder is an elected fellow of the American Statistical Association and the Institute of Mathematical Statistics.
Dr. Roeder’s specific research interests include developing statistical tools for finding associations between patterns of genetic variation and complex disease. To solve biologically relevant problems, Dr. Rodeder’ research group use modern statistical methods such as high dimensional statistics, statistical machine learning, nonparametric methods and networks. Data arises primarily from Next Generation Sequencing and gene expression arrays. The group’s methodological work is motivated by studies of schizophrenia, autism and other genetic disorders.