SRTsim – spatial pattern preserving simulations for spatially resolved transcriptomics

Spatially resolved transcriptomics (SRT)-specific computational methods are often developed, tested, validated, and evaluated in silico using simulated data. Unfortunately, existing simulated SRT data are often poorly documented, hard to reproduce, or unrealistic. Single-cell simulators are not directly applicable for SRT simulation as they cannot incorporate spatial information. University of Michigan researchers have developed SRTsim, an SRT-specific simulator for scalable, reproducible, and realistic SRT simulations. SRTsim not only maintains various expression characteristics of SRT data but also preserves spatial patterns. The researchers illustrate the benefits of SRTsim in benchmarking methods for spatial clustering, spatial expression pattern detection, and cell-cell communication identification.

A schematic of SRTsim

Fig. 1

SRTsim is a flexible SRT simulator that can perform either reference-based or reference-free simulations. Both types of simulations can be carried out in a tissue-based or a domain-specific fashion. In the reference-based simulations, SRTsim requires a reference SRT data in the form of a gene expression count matrix, a location matrix, and, for domain-specific simulations, an additional domain annotation matrix. SRTsim can directly use the reference data locations or create new locations and can redesign the target tissue region (Step 1). SRTsim then fits an appropriate count distribution to each gene in the reference and simulates the gene-specific counts in the synthetic data (Step 2). Finally, SRTsim assigns the simulated counts to the locations in the synthetic data in a way that preserves the spatial expression pattern observed in the reference data. In reference-free simulations, SRTsim allows users to design spatial patterns either from a customized shape or a predefined shape of interest and generate synthetic data with user-specified model parameters. P, Poisson; NB, negative binomial; ZIP, zero-inflated Poisson; ZINB, zero-inflated negative binomial

Availability – The R package SRTsim is also available at CRAN:

Zhu J, Shang L, Zhou X. (2023) SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics. Genome Biol 24(1):39. [article]

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