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

ABSTRACT: Profiling tumors with single-cell RNA sequencing (scRNA-seq) has the potential to identify recurrent patterns of transcription variation related to cancer progression, and produce new therapeutically relevant insights. However, the presence of strong inter-tumor heterogeneity often obscures more subtle patterns that are shared across tumors, some of which may characterize clinically relevant disease subtypes. Here we introduce a new statistical method, generalized binary covariance decomposition (GBCD), to address this problem. We show that GBCD can help 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

SUBMITTER: Liu Y 

PROVIDER: S-EPMC10462040 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature