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 intertumor heterogeneity can obscure more subtle patterns that are shared across tumors. Here we introduce a statistical method, generalized binary covariance decomposition (GBCD), to address this problem. We show that 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 intertumor heterogeneity, it can produce more interpretable results than existing methods. Applied to data on pancreatic ductal adenocarcinoma, GBCD

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12035913 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature