A benchmarking study of copy number variation inference methods using single-cell RNA-sequencing data.

ABSTRACT:

Background

Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful tool for cancer research, enabling in-depth characterization of tumor heterogeneity at the single-cell level. Recently, several scRNA-seq copy number variation (scCNV) inference methods have been developed, expanding the application of scRNA-seq to study genetic heterogeneity in cancer using transcriptomic data. However, the fidelity of these methods has not been investigated systematically.

Methods

We benchmarked five commonly used scCNV inference methods: HoneyBADGER, CopyKAT, CaSpER, inferCNV, and sciCNV. We evaluated their performance across four different scRNA-seq platforms using data from our previous multicenter study. We evaluated scCNV performance further using scRNA-seq datasets derived fr

SUBMITTER: Chen X 

PROVIDER: S-EPMC12204187 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature