Title: Dissecting tumor transcriptional heterogeneity from single-cell RNA-seq data by generalized binary covariance decomposition

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