Dissecting Tumor Transcriptional Heterogeneity from Single-cell RNA-Seq Data by Generalized Binary Covariance Decomposition
Yusha Liu, PhD
Assistant Professor
Department of Biostatistics | UNC-Chapel Hill
WHEN:聽Wednesday, March 26, 2025, from 3:30 to 4:30 p.m.
WHERE:聽Hybrid | 2001 不良研究所 College Avenue, Room 1140;
NOTE:聽Yusha Liu will be presenting from Chapel Hill
Abstract
Profiling tumors with single-cell RNA sequencing has the potential to identify recurrent patterns of transcription variation related to cancer progression, and to produce therapeutically relevant insights. However, strong inter-tumor heterogeneity can obscure more subtle patterns that are shared across tumors. In this talk, I will introduce a novel statistical method, generalized binary covariance decomposition (GBCD), to address this problem. GBCD can decompose transcriptional heterogeneity into interpretable components鈥攊ncluding patient-specific, dataset-specific and shared components relevant to disease subtypes鈥攁nd that, in the presence of strong inter-tumor heterogeneity, it can produce more interpretable results than existing methods. Applied to data on pancreatic ductal adenocarcinoma, GBCD produced a refined characterization of existing tumor subtypes, and identified a gene expression program prognostic of poor survival independent of tumor stage and subtype. The gene expression program is enriched for genes involved in stress responses, and suggests a role for the integrated stress response in pancreatic ductal adenocarcinoma.
Speaker Bio
Dr. Yusha Liu is a research assistant professor at the Department of Biostatistics at the University of North Carolina at Chapel Hill. She received her PhD in Statistics from Rice University and postdoctoral training from the Department of Human Genetics at the University of Chicago. Dr. Liu鈥檚 research interests lie at the intersection of statistics and cancer biology, and she is particularly interested in developing and applying flexible and scalable statistical approaches to analyzing large-scale and complex genomics data, such as single cell data, and ultimately contributing to the understanding of cancer etiology and the development of effective prevention strategies and targeted therapies.