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scSpatialSIM: a simulator of spatial single-cell molecular data.

Alex C Soupir1,2, Julia Wrobel3, Jordan H Creed1

  • 1Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, United States.

Softwarex
|March 30, 2026
PubMed
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scSpatialSIM is an R package that simulates realistic spatial single-cell molecular data for benchmarking analysis methods. It aids in developing new tools to understand tissue architecture and cellular interactions.

Area of Science:

  • Spatial biology
  • Computational biology
  • Bioinformatics

Background:

  • Advancements in spatial molecular technologies like multiplex immunofluorescence (mIF) and spatial transcriptomics (SRT) necessitate robust statistical methods for tissue spatial architecture analysis.
  • A lack of standardized "gold standard" approaches hinders effective benchmarking and comparison of these analytical methods.

Purpose of the Study:

  • To develop "scSpatialSIM", an R package for simulating biologically realistic spatial single-cell molecular data.
  • To provide a flexible framework for generating diverse spatial data without requiring reference datasets, enabling efficient simulation of cell clustering, co-localization, and tissue features.

Main Methods:

  • Developed "scSpatialSIM", an R package for simulating spatial single-cell molecular data.
Keywords:
Cell clusteringCell co-localizationSpatial molecular dataSpatial single-cell simulationsSpatial statistics benchmarking

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  • Incorporated features for cell clustering, co-localization, tissue compartments, and tissue holes.
  • Supported simulation of categorical and continuous data, integrating with existing R packages for downstream analysis.

    • Main Results:

    • Applied "scSpatialSIM" to benchmark spatial point pattern summary functions (Ripley's K(r), G(r), g(r)).
    • Ripley's K(r) demonstrated consistent detection of clustering across various radii, showing superior sensitivity and robustness compared to other methods.
    • The package effectively simulates cell clustering and co-localization patterns.

    Conclusions:

    • "scSpatialSIM" offers a flexible and scalable platform for generating spatial data, facilitating the comparative evaluation of spatial statistics.
    • The package supports the development of novel methods for characterizing tissue spatial organization and advancing spatial molecular research.
    • Enables researchers to explore hypothetical scenarios and gain insights into tissue architecture and cellular interactions.