HSPH Biostatistics and DFCI Data Science Colloquium
Tuesday March 11th at 4:00pm
HSPH FXB G12

Yusha Liu, PhD
Research Assistant Professor
Department of Biostatistics
The University of North Carolina at Chapel Hill

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 — including patient-specific, dataset-specific and shared components relevant to disease subtypes — and 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.