Benchmarking copy number alteration inference methods for spatial transcriptomics

Copy number alterations (CNAs), gains or losses of genomic regions, contribute to malignant progression and tumor heterogeneity. Advances in spatial transcriptomics have expanded opportunities to study clonal structure in situ, but direct spatial genomic profiling remains difficult in practice, motivating the increasing use of computational methods to infer CNAs from spatial transcriptomics data. However, their performance across diverse spatial transcriptomics settings remains unclear. Here, we present a benchmark of nine CNA inference methods across 69 spatial transcriptomics tissue sections from six cancer types and four spatial transcriptomics platforms. By evaluating these methods across four key tasks, we show that no single method consistently outperforms all others, with performance depending on the analytical goal and data characteristics. We therefore provide task-specific and data-aware guidance to help users select appropriate methods in practical settings. More broadly, this benchmark provides a basis for the future development and optimization of CNA inference methods. Copy number alteration inference from spatial transcriptomics remains challenging. Here, the authors benchmark nine methods across a variety of tissue sections, showing that no method dominates across all tasks and providing practical guidance for method selection.