Which Tool Tells the Truth? A Head-to-Head Benchmarking of scRNA-seq CNV Methods
Tumors are often genetic mosaics, harboring diverse cell populations shaped by mutations like copy number variations (CNVs). These genomic alterations drive cancer evolution--but detecting them at single-cell resolution remains challenging. In this study, scientists systematically evaluated five widely used tools that infer CNVs from single-cell RNA sequencing (scRNA-seq) data. The analysis uncovered dramatic differences in performance among methods, depending on data type and experimental design. Notably, CaSpER and CopyKAT emerged as top performers for CNV inference, while inferCNV and CopyKAT excelled in identifying tumor subpopulations. This comprehensive comparison provides a valuable roadmap for researchers aiming to decode cancer heterogeneity more precisely.