stCNASim: Allele-aware spatial RNA-seq simulator enables systematic benchmarking of copy number inference
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…
Spatial transcriptomics (ST) is transforming tumor evolution research by allowing detailed mapping of copy-number alterations (CNA). However, assessing the performance of existing single-cell (SC) and ST-specific CNA inference tools is difficult because of the lack of ground-truth datasets. The research team has introduced stCNASim, an allele-aware spatial RNA-seq simulator that creates realistic raw reads in spatial contexts.
By generating 46 benchmarking datasets under various technical conditions and spatial architectures, the team tested five popular computational methods. Their findings show that while SC-based methods perform well with ST data, ST-specific methods can leverage spatial autocorrelation but face challenges with high spatial intermixing.
The allele-aware methods CalicoST, Numbat, and XClone demonstrated superior performance in extreme situations, but each had specific sensitivities to factors like low purity, mirrored alleles, and low coverage. This study introduces a scalable simulator and a comprehensive benchmark, paving the way for improved spatial CNA analysis tools.
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