Spider: a flexible and unified framework for simulating spatial transcriptomics data

Abstract Spatial transcriptomics technology provides a valuable view for studying cellular heterogeneity due to its ability to simultaneously acquire gene expression profile and cell location information. However, benchmarking these rapidly accumulating spatial transcriptomics analysis tools is challenging owing to the limited diversity and accuracy of “gold standard” data sets annotated by pathologists. To address this issue, we proposed Spider, a flexible and unified simulator for spatial transcriptomics data guided by cell type proportion and transition matrix of adjacent cell types. Taking advantage of a heuristic batched simulated annealing algorithm (BSA) in assigning simulated cell type labels, Spider can generate spatial transcriptomics data for one million cells in just five minutes. Furthermore, Spider can generate various types of spatial transcriptomics data, including immune hot/cold tumor samples by specifying different immune cell proportions and transition matrices and layered tissue samples via an interactive interface. In addition, Spider is also a unified framework for ST data simulation in which we have implemented diverse simulators proposed by other researchers as special cases. We have systematically evaluated the performance of Spider and competing tools, and demonstrated Spider’s remarkable power to capture the spatial pattern of the reference dataset. Spider is available at https://github.com/YANG-ERA/Artist .