A Bayesian framework to study tumor subclone-specific expression by combining bulk DNA and single-cell RNA sequencing data.

ABSTRACT: Genetic and gene expression heterogeneity is an essential hallmark of many tumors, allowing the cancer to evolve and to develop resistance to treatment. Currently, the most commonly used data types for studying such heterogeneity are bulk tumor/normal whole-genome or whole-exome sequencing (WGS, WES); and single-cell RNA sequencing (scRNA-seq), respectively. However, tools are currently lacking to link genomic tumor subclonality with transcriptomic heterogeneity by integrating genomic and single-cell transcriptomic data collected from the same tumor. To address this gap, we developed scBayes, a Bayesian probabilistic framework that uses tumor subclonal structure inferred from bulk DNA sequencing data to determine the subclonal identity of cells from single-cell gene expression (scRNA-seq)

SUBMITTER: Qiao Y 

PROVIDER: S-EPMC10903947 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature