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

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Spatially resolved transcriptomics (SRT) enables the study of cellular neighborhoods within tissues.
• Evaluating analytical tools for SRT requires synthetic datasets with known cell-cell interaction features.
• Existing tools lack the ability to generate realistic SRT data reflecting complex interactions.

Purpose of the Study:

• To introduce sCCIgen, a novel real-data-based simulator for generating high-fidelity SRT data.
• To provide a tool for creating synthetic SRT datasets with a focus on cell-cell interactions.
• To facilitate the development and evaluation of analytical methods for SRT data.

Main Methods:

• sCCIgen utilizes real SRT data to model transcriptomic and spatial characteristics.
• The simulator incorporates features such as cell colocalization and spatial gene expression dependence.
• It also models gene-gene interactions between neighboring cells.

Main Results:

• sCCIgen generates realistic synthetic SRT data with preserved transcriptomic and spatial features.
• The simulator accurately models various cell-cell interaction-induced features.
• The implemented tool is interactive, user-friendly, and well-documented.

Conclusions:

• sCCIgen addresses the need for high-fidelity synthetic SRT data generation.
• This tool supports research in cellular interactions and spatial biology.
• sCCIgen enhances the development and validation of computational tools for SRT analysis.