stCNASim: Allele-aware spatial RNA-seq simulator enables systematic benchmarking of copy number inference

Abstract

Spatial transcriptomics (ST) is revolutionizing the study of tumor evolution by enabling spatially resolved copy-number alteration (CNA) analysis. However, evaluating the accuracy and robustness of current single-cell (SC) and ST-specific CNA inference tools remains challenging due to the absence of ground-truth datasets. Here, we present stCNASim, an allele-aware spatial RNA-seq simulator that generates raw reads within realistic spatial contexts. We synthesized 46 benchmarking datasets across varying technical settings and spatial architectures to evaluate five widely used computational methods. Our analysis reveals that while SC-based methods adapt well to ST data, ST-specific methods successfully benefit from considering spatial autocorrelation but struggle under high spatial intermixing. The allele-aware methods CalicoST, Numbat, and XClone achieved top-tier performance with unique advantages in extreme scenarios, yet showed distinct sensitivities to low purity, mirrored alleles, and low coverage, respectively. By providing a scalable simulator and a rigorous benchmark, this work establishes a much-needed framework to guide and accelerate future tool development in spatial CNA analysis.