sCCIgen: A high-fidelity spatially resolved transcriptomics data simulator for cell-cell interaction studies.

1. Introduction

Spatially resolved transcriptomics (SRT) facilitates the study of cell-cell interactions within native tissue environments.

To support method development and benchmarking, we introduce sCCIgen, a real-data-based simulator that generates high-fidelity synthetic SRT data with known interaction features. sCCIgen preserves transcriptomic and spatial characteristics and provides key interaction features, including cell colocalization, spatial dependence of gene expression, and gene-gene interactions between neighboring cells.

It supports input from SRT data, single-cell expression data alone, and unpaired expression and SRT data. sCCIgen is interactive, user-friendly, reproducible, and well-documented for studying cellular interactions and spatial biology.

2. Reference

• Song, X., et al. sCCIgen: A high-fidelity spatially resolved transcriptomics data simulator for cell-cell interaction studies. bioRxiv (2025)

3. Software Instruction

The installation instruction, documentation, tutorials, and examples are accessible at https://songxiaoyu.github.io/sCCIgen/.

4. Brief overview of simulation options

Users have two options to perform SRT simulations using sCCIgen. Depending on preference, Users can choose to use:

• An interactive interface. The interface helps users to choose tasks, data, and parameters, and lets sCCIgen run simulations to provide simulated data, together with parameters and documentations, saved in the working directory. As simulations usually involve many parameters, this option is highly recommended for the first time user or researchers with limited programming expertise.
  • Tutorials for using the interface to simulate SRT.
• R package. A direct use of sCCIgen package offers greater flexibility, allowing users to adjust more parameters that would otherwise be fixed at default values. It also enables the output of simulation byproducts, such as spatial windows. This approach is recommended for users with goode programming experience who wish to perform customized simulations.
  • Tutorials for using the R package to simulate SRT based on snRNAseq data.
  • Tutorials for using the R package to simulate SRT based on single-cell SRT data (single region).
  • Tutorials for using the R package to simulate SRT based on single-cell SRT data (multiple regions).
  • Tutorials for using the R package to simulate SRT based on single-cell expression and unpaired spatial data.