Advancements in spatial transcriptomics and single-cell RNA sequencing have enhanced our understanding of gene expression within tissues. Spatial transcriptomics retains spatial context at the expense of resolution, often resulting in cell mixtures, whereas single-cell RNA sequencing offers single-cell resolution with the loss of spatial information. Some computational methods aim to integrate data from these two technologies; however, a ground truth for their evaluation is typically lacking. Thus, simulation techniques may be used to generate artificial gold or silver standards, offering the possibility for standardized analysis. This not only requires an accurate replication of real tissue types, but also sufficient sample diversity, calling for a unified evaluation of these properties between present techniques. Existing benchmark metrics and platforms often favor simulations that closely replicate the input data rather than promoting novel tissue layouts. This paper introduces a comprehensive benchmarking platform that evaluates spatial transcriptomics simulation methods across data property distributions, biological signal preservation, and similarity-based metrics. Our framework ensures that simulations go beyond simple data replication, instead introducing biologically meaningful variation. BEASTsim can be easily integrated into analysis pipelines and provides a practical tool for evaluating and developing computational methods, thereby advancing the integration of spatial transcriptomics and single-cell RNA sequencing data to yield more accurate biological insights. As a result, we have utilized BEASTsim to create a decision tree that helps users select the most suitable simulation model based on their data and goals. This work provides a practical tool for evaluating and developing computational methods, thereby advancing the integration of spatial transcriptomics and single-cell RNA sequencing data to yield more accurate biological insights.
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