SRTsim

SRTsim simulates realistic spatially resolved transcriptomics (SRT) datasets that preserve gene expression characteristics and spatial patterns for benchmarking and developing analytical methods.

Key Features:

• Spatial information integration: Incorporates spatial coordinates into simulated transcriptomic profiles to model spatial patterns inherent to SRT data.
• Preservation of expression characteristics and spatial patterns: Maintains diverse gene expression distributions while conserving spatial expression patterns observed in real SRT datasets.
• Scalability and reproducibility: Supports large-scale simulations and produces reproducible simulated datasets.

Scientific Applications:

• Spatial clustering: Provides realistic spatial transcriptomic data for evaluating and comparing clustering algorithms that group spots or cells by spatially informed expression profiles.
• Spatial expression pattern detection: Enables testing of methods designed to identify spatially variable genes and region-specific expression patterns across tissue sections.
• Cell-cell communication identification: Facilitates development and validation of computational approaches for inferring intercellular signaling and communication in a spatial context.

Methodology:

Generates simulated SRT data that reflect biological expression complexity and incorporate realistic spatial configurations.